April 3, 2024April 7, 2024 by David D. Nolte

A Short History of Chaos Theory

Chaos and Complexity, History of Dynamics
Bifurcation, Chaos Theory, Henri Poincaré, History of Physics, KAM Theory, Kolmogorov, Logistic Map, Nonlinear Dynamics, Short History, V. I. Arnold

Chaos seems to rule our world. Weather events, natural disasters, economic volatility, empire building—all these contribute to the complexities that buffet our lives. It is no wonder that ancient man attributed the chaos to the gods or to the fates, infinitely far from anything we can comprehend as cause and effect. Yet there is a balm to soothe our wounds from the slings of life—Chaos Theory—if not to solve our problems, then at least to understand them.

(Sections of this Blog have been excerpted

from the book Galileo Unbound, published by Oxford University Press)

Chaos Theory is the theory of complex systems governed by multiple factors that produce complicated outputs. The power of the theory is its ability recognize when the complicated outputs are not “random”, no matter how complicated they are, but are in fact determined by the inputs. Furthermore, chaos theory finds structures and patterns within the output—like the fractal structures known as “strange attractors”. These patterns not only are not random, but they tell us about the internal mechanics of the system, and they tell us where to look “on average” for the system behavior.

In other words, chaos theory tames the chaos, and we no longer need to blame gods or the fates.

Henri Poincare (1889)

The first glimpse of the inner workings of chaos was made by accident when Henri Poincaré responded to a mathematics competition held in honor of the King of Sweden. The challenge was to prove whether the solar system was absolutely stable, or whether there was a danger that one day the Earth would be flung from its orbit. Poincaré had already been thinking about the stability of dynamical systems so he wrote up his solution to the challenge and sent it in, believing that he had indeed proven that the solar system was stable.

His entry to the competition was the most convincing, so he was awarded the prize and instructed to submit the manuscript for publication. The paper was already at the printers and coming off the presses when Poincaré was asked by the competition organizer to check one last part of the proof which one of the reviewer’s had questioned relating to homoclinic orbits.

Fig. 1 A homoclinic orbit is an orbit in phase space that intersects itself.

To Poincaré’s horror, as he checked his results against the reviewer’s comments, he found that he had made a fundamental error, and in fact the solar system would never be stable. The problem that he had overlooked had to do with the way that orbits can cross above or below each other on successive passes, leading to a tangle of orbital trajectories that crisscrossed each other in a fine mesh. This is known as the “homoclinic tangle”: it was the first glimpse that deterministic systems could lead to unpredictable results. Most importantly, he had developed the first mathematical tools that would be needed to analyze chaotic systems—such as the Poincaré section—but nearly half a century would pass before these tools would be picked up again.

Poincaré paid out of his own pocket for the first printing to be destroyed and for the corrected version of his manuscript to be printed in its place [1]. No-one but the competition organizers and reviewers ever saw his first version. Yet it was when he was correcting his mistake that he stumbled on chaos for the first time, which is what posterity remembers him for. This little episode in the history of physics went undiscovered for a century before being brought to light by Barrow-Green in her 1997 book Poincaré and the Three Body Problem [2].

Fig. 2 Henri Poincaré’s homoclinic tangle from the Standard Map. (The picture on the right is the Poincaré crater on the moon). For more details, see my blog on Poincaré and his Homoclinic Tangle.

Cartwight and Littlewood (1945)

During World War II, self-oscillations and nonlinear dynamics became strategic topics for the war effort in England. High-power magnetrons were driving long-range radar, keeping Britain alert to Luftwaffe bombing raids, and the tricky dynamics of these oscillators could be represented as a driven van der Pol oscillator. These oscillators had been studied in the 1920’s by the Dutch physicist Balthasar van der Pol (1889–1959) when he was completing his PhD thesis at the University of Utrecht on the topic of radio transmission through ionized gases. van der Pol had built a short-wave triode oscillator to perform experiments on radio diffraction to compare with his theoretical calculations of radio transmission. Van der Pol’s triode oscillator was an engineering feat that produced the shortest wavelengths of the day, making van der Pol intimately familiar with the operation of the oscillator, and he proposed a general form of differential equation for the triode oscillator.

Fig. 3 Driven van der Pol oscillator equation.

Research on the radar magnetron led to theoretical work on driven nonlinear oscillators, including the discovery that a driven van der Pol oscillator could break up into wild and intermittent patterns. This “bad” behavior of the oscillator circuit (bad for radar applications) was the first discovery of chaotic behavior in man-made circuits.

These irregular properties of the driven van der Pol equation were studied by Mary- Lucy Cartwright (1990–1998) (the first woman to be elected a fellow of the Royal Society) and John Littlewood (1885–1977) at Cambridge who showed that the coexistence of two periodic solutions implied that discontinuously recurrent motion—in today’s parlance, chaos— could result, which was clearly undesirable for radar applications. The work of Cartwright and Littlewood [3] later inspired the work by Levinson and Smale as they introduced the field of nonlinear dynamics.

Andrey Kolmogorov (1954)

The passing of the Russian dictator Joseph Stalin provided a long-needed opening for Soviet scientists to travel again to international conferences where they could meet with their western colleagues to exchange ideas. Four Russian mathematicians were allowed to attend the 1954 International Congress of Mathematics (ICM) held in Amsterdam, the Netherlands. One of those was Andrey Nikolaevich Kolmogorov (1903 – 1987) who was asked to give the closing plenary speech. Despite the isolation of Russia during the Soviet years before World War II and later during the Cold War, Kolmogorov was internationally renowned as one of the greatest mathematicians of his day.

By 1954, Kolmogorov’s interests had spread into topics in topology, turbulence and logic, but no one was prepared for the topic of his plenary lecture at the ICM in Amsterdam. Kolmogorov spoke on the dusty old topic of Hamiltonian mechanics. He even apologized at the start for speaking on such an old topic when everyone had expected him to speak on probability theory. Yet, in the length of only half an hour he laid out a bold and brilliant outline to a proof that the three-body problem had an infinity of stable orbits. Furthermore, these stable orbits provided impenetrable barriers to the diffusion of chaotic motion across the full phase space of the mechanical system. The crucial consequences of this short talk were lost on almost everyone who attended as they walked away after the lecture, but Kolmogorov had discovered a deep lattice structure that constrained the chaotic dynamics of the solar system.

Kolmogorov’s approach used a result from number theory that provides a measure of how close an irrational number is to a rational one. This is an important question for orbital dynamics, because whenever the ratio of two orbital periods is a ratio of integers, especially when the integers are small, then the two bodies will be in a state of resonance, which was the fundamental source of chaos in Poincaré’s stability analysis of the three-body problem. After Komogorov had boldly presented his results at the ICM of 1954 [4], what remained was the necessary mathematical proof of Kolmogorov’s daring conjecture. This would be provided by one of his students, V. I. Arnold, a decade later. But before the mathematicians could settle the issue, an atmospheric scientist, using one of the first electronic computers, rediscovered Poincaré’s tangle, this time in a simplified model of the atmosphere.

Edward Lorenz (1963)

In 1960, with the help of a friend at MIT, the atmospheric scientist Edward Lorenz purchased a Royal McBee LGP-30 tabletop computer to make calculation of a simplified model he had derived for the weather. The McBee used 113 of the latest miniature vacuum tubes and also had 1450 of the new solid-state diodes made of semiconductors rather than tubes, which helped reduce the size further, as well as reducing heat generation. The McBee had a clock rate of 120 kHz and operated on 31-bit numbers with a 15 kB memory. Under full load it used 1500 Watts of power to run. But even with a computer in hand, the atmospheric equations needed to be simplified to make the calculations tractable. Lorenz simplified the number of atmospheric equations down to twelve, and he began programming his Royal McBee.

Progress was good, and by 1961, he had completed a large initial numerical study. One day, as he was testing his results, he decided to save time by starting the computations midway by using mid-point results from a previous run as initial conditions. He typed in the three-digit numbers from a paper printout and went down the hall for a cup of coffee. When he returned, he looked at the printout of the twelve variables and was disappointed to find that they were not related to the previous full-time run. He immediately suspected a faulty vacuum tube, as often happened. But as he looked closer at the numbers, he realized that, at first, they tracked very well with the original run, but then began to diverge more and more rapidly until they lost all connection with the first-run numbers. The internal numbers of the McBee had a precision of 6 decimal points, but the printer only printed three to save time and paper. His initial conditions were correct to a part in a thousand, but this small error was magnified exponentially as the solution progressed. When he printed out the full six digits (the resolution limit for the machine), and used these as initial conditions, the original trajectory returned. There was no mistake. The McBee was working perfectly.

At this point, Lorenz recalled that he “became rather excited”. He was looking at a complete breakdown of predictability in atmospheric science. If radically different behavior arose from the smallest errors, then no measurements would ever be accurate enough to be useful for long-range forecasting. At a more fundamental level, this was a break with a long-standing tradition in science and engineering that clung to the belief that small differences produced small effects. What Lorenz had discovered, instead, was that the deterministic solution to his 12 equations was exponentially sensitive to initial conditions (known today as SIC).

The more Lorenz became familiar with the behavior of his equations, the more he felt that the 12-dimensional trajectories had a repeatable shape. He tried to visualize this shape, to get a sense of its character, but it is difficult to visualize things in twelve dimensions, and progress was slow, so he simplified his equations even further to three variables that could be represented in a three-dimensional graph [5].

Fig. 5 Two-dimensional projection of the three-dimensional Lorenz Butterfly.

V. I. Arnold (1964)

Meanwhile, back in Moscow, an energetic and creative young mathematics student knocked on Kolmogorov’s door looking for an advisor for his undergraduate thesis. The youth was Vladimir Igorevich Arnold (1937 – 2010), who showed promise, so Kolmogorov took him on as his advisee. They worked on the surprisingly complex properties of the mapping of a circle onto itself, which Arnold filed as his dissertation in 1959. The circle map holds close similarities with the periodic orbits of the planets, and this problem led Arnold down a path that drew tantalizingly close to Kolmogorov’s conjecture on Hamiltonian stability. Arnold continued in his PhD with Kolmogorov, solving Hilbert’s 13th problem by showing that every function of n variables can be represented by continuous functions of a single variable. Arnold was appointed as an assistant in the Faculty of Mechanics and Mathematics at Moscow State University.

Arnold’s habilitation topic was Kolmogorov’s conjecture, and his approach used the same circle map that had played an important role in solving Hilbert’s 13th problem. Kolmogorov neither encouraged nor discouraged Arnold to tackle his conjecture. Arnold was led to it independently by the similarity of the stability problem with the problem of continuous functions. In reference to his shift to this new topic for his habilitation, Arnold stated “The mysterious interrelations between different branches of mathematics with seemingly no connections are still an enigma for me.” [6]

Arnold began with the problem of attracting and repelling fixed points in the circle map and made a fundamental connection to the theory of invariant properties of action-angle variables . These provided a key element in the proof of Kolmogorov’s conjecture. In late 1961, Arnold submitted his results to the leading Soviet physics journal—which promptly rejected it because he used forbidden terms for the journal, such as “theorem” and “proof”, and he had used obscure terminology that would confuse their usual physicist readership, terminology such as “Lesbesgue measure”, “invariant tori” and “Diophantine conditions”. Arnold withdrew the paper.

Arnold later incorporated an approach pioneered by Jurgen Moser [7] and published a definitive article on the problem of small divisors in 1963 [8]. The combined work of Kolmogorov, Arnold and Moser had finally established the stability of irrational orbits in the three-body problem, the most irrational and hence most stable orbit having the frequency of the golden mean. The term “KAM theory”, using the first initials of the three theorists, was coined in 1968 by B. V. Chirikov, who also introduced in 1969 what has become known as the Chirikov map (also known as the Standard map ) that reduced the abstract circle maps of Arnold and Moser to simple iterated functions that any student can program easily on a computer to explore KAM invariant tori and the onset of Hamiltonian chaos, as in Fig. 1 [9].

Fig. 6 The Chirikov Standard Map when the last stable orbits are about to dissolve for ε = 0.97.

Sephen Smale (1967)

Stephen Smale was at the end of a post-graduate fellowship from the National Science Foundation when he went to Rio to work with Mauricio Peixoto. Smale and Peixoto had met in Princeton in 1960 where Peixoto was working with Solomon Lefschetz (1884 – 1972) who had an interest in oscillators that sustained their oscillations in the absence of a periodic force. For instance, a pendulum clock driven by the steady force of a hanging weight is a self-sustained oscillator. Lefschetz was building on work by the Russian Aleksandr A. Andronov (1901 – 1952) who worked in the secret science city of Gorky in the 1930’s on nonlinear self-oscillations using Poincaré’s first return map. The map converted the continuous trajectories of dynamical systems into discrete numbers, simplifying problems of feedback and control.

The central question of mechanical control systems, even self-oscillating systems, was how to attain stability. By combining approaches of Poincaré and Lyapunov, as well as developing their own techniques, the Gorky school became world leaders in the theory and applications of nonlinear oscillations. Andronov published a seminal textbook in 1937 The Theory of Oscillations with his colleagues Vitt and Khaykin, and Lefschetz had obtained and translated the book into English in 1947, introducing it to the West. When Peixoto returned to Rio, his interest in nonlinear oscillations captured the imagination of Smale even though his main mathematical focus was on problems of topology. On the beach in Rio, Smale had an idea that topology could help prove whether systems had a finite number of periodic points. Peixoto had already proven this for two dimensions, but Smale wanted to find a more general proof for any number of dimensions.

Norman Levinson (1912 – 1975) at MIT became aware of Smale’s interests and sent off a letter to Rio in which he suggested that Smale should look at Levinson’s work on the triode self-oscillator (a van der Pol oscillator), as well as the work of Cartwright and Littlewood who had discovered quasi-periodic behavior hidden within the equations. Smale was puzzled but intrigued by Levinson’s paper that had no drawings or visualization aids, so he started scribbling curves on paper that bent back upon themselves in ways suggested by the van der Pol dynamics. During a visit to Berkeley later that year, he presented his preliminary work, and a colleague suggested that the curves looked like strips that were being stretched and bent into a horseshoe.

Smale latched onto this idea, realizing that the strips were being successively stretched and folded under the repeated transformation of the dynamical equations. Furthermore, because dynamics can move forward in time as well as backwards, there was a sister set of horseshoes that were crossing the original set at right angles. As the dynamics proceeded, these two sets of horseshoes were repeatedly stretched and folded across each other, creating an infinite latticework of intersections that had the properties of the Cantor set. Here was solid proof that Smale’s original conjecture was wrong—the dynamics had an infinite number of periodicities, and they were nested in self-similar patterns in a latticework of points that map out a Cantor-like set of points. In the two-dimensional case, shown in the figure, the fractal dimension of this lattice is D = ln4/ln3 = 1.26, somewhere in dimensionality between a line and a plane. Smale’s infinitely nested set of periodic points was the same tangle of points that Poincaré had noticed while he was correcting his King Otto Prize manuscript. Smale, using modern principles of topology, was finally able to put rigorous mathematical structure to Poincaré’s homoclinic tangle. Coincidentally, Poincaré had launched the modern field of topology, so in a sense he sowed the seeds to the solution to his own problem.

Fig. 7 The horseshoe takes regions of phase space and stretches and folds them over and over to create a lattice of overlapping trajectories.

Ruelle and Takens (1971)

The onset of turbulence was an iconic problem in nonlinear physics with a long history and a long list of famous researchers studying it. As far back as the Renaissance, Leonardo da Vinci had made detailed studies of water cascades, sketching whorls upon whorls in charcoal in his famous notebooks. Heisenberg, oddly, wrote his PhD dissertation on the topic of turbulence even while he was inventing quantum mechanics on the side. Kolmogorov in the 1940’s applied his probabilistic theories to turbulence, and this statistical approach dominated most studies up to the time when David Ruelle and Floris Takens published a paper in 1971 that took a nonlinear dynamics approach to the problem rather than statistical, identifying strange attractors in the nonlinear dynamical Navier-Stokes equations [10]. This paper coined the phrase “strange attractor”. One of the distinct characteristics of their approach was the identification of a bifurcation cascade. A single bifurcation means a sudden splitting of an orbit when a parameter is changed slightly. In contrast, a bifurcation cascade was not just a single Hopf bifurcation, as seen in earlier nonlinear models, but was a succession of Hopf bifurcations that doubled the period each time, so that period-two attractors became period-four attractors, then period-eight and so on, coming fast and faster, until full chaos emerged. A few years later Gollub and Swinney experimentally verified the cascade route to turbulence , publishing their results in 1975 [11].

Fig. 8 Bifurcation cascade of the logistic map.

Feigenbaum (1978)

In 1976, computers were not common research tools, although hand-held calculators now were. One of the most famous of this era was the Hewlett-Packard HP-65, and Feigenbaum pushed it to its limits. He was particularly interested in the bifurcation cascade of the logistic map [12]—the way that bifurcations piled on top of bifurcations in a forking structure that showed increasing detail at increasingly fine scales. Feigenbaum was, after all, a high-energy theorist and had overlapped at Cornell with Kenneth Wilson when he was completing his seminal work on the renormalization group approach to scaling phenomena. Feigenbaum recognized a strong similarity between the bifurcation cascade and the ideas of real-space renormalization where smaller and smaller boxes were used to divide up space.

One of the key steps in the renormalization procedure was the need to identify a ratio of the sizes of smaller structures to larger structures. Feigenbaum began by studying how the bifurcations depended on the increasing growth rate. He calculated the threshold values rm for each of the bifurcations, and then took the ratios of the intervals, comparing the previous interval (r_m-1 – r_m-2) to the next interval (r_m – r_m-1). This procedure is like the well-known method to calculate the golden ratio = 1.61803 from the Fibonacci series, and Feigenbaum might have expected the golden ratio to emerge from his analysis of the logistic map. After all, the golden ratio has a scary habit of showing up in physics, just like in the KAM theory. However, as the bifurcation index m increased in Feigenbaum’s study, this ratio settled down to a limiting value of 4.66920. Then he did what anyone would do with an unfamiliar number that emerges from a physical calculation—he tried to see if it was a combination of other fundamental numbers, like pi and Euler’s constant e, and even the golden ratio. But none of these worked. He had found a new number that had universal application to chaos theory [13].

Fig. 9 The ratio of the limits of successive cascades leads to a new universal number (the Feigenbaum number).

Gleick (1987)

By the mid-1980’s, chaos theory was seeping in to a broadening range of research topics that seemed to span the full breadth of science, from biology to astrophysics, from mechanics to chemistry. A particularly active group of chaos practitioners were J. Doyn Farmer, James Crutchfield, Norman Packard and Robert Shaw who founded the Dynamical Systems Collective at the University of California, Santa Cruz. One of the important outcomes of their work was a method to reconstruct the state space of a complex system using only its representative time series [14]. Their work helped proliferate the techniques of chaos theory into the mainstream. Many who started using these techniques were only vaguely aware of its long history until the science writer James Gleick wrote a best-selling history of the subject that brought chaos theory to the forefront of popular science [15]. And the rest, as they say, is history.

References

[1] Poincaré, H. and D. L. Goroff (1993). New methods of celestial mechanics. Edited and introduced by Daniel L. Goroff. New York, American Institute of Physics.

[2] J. Barrow-Green, Poincaré and the three body problem (London Mathematical Society, 1997).

[3] Cartwright,M.L.andJ.E.Littlewood(1945).“Onthenon-lineardifferential equation of the second order. I. The equation y′′ − k(1 – yˆ2)y′ + y = bλk cos(λt + a), k large.” Journal of the London Mathematical Society 20: 180–9. Discussed in Aubin, D. and A. D. Dalmedico (2002). “Writing the History of Dynamical Systems and Chaos: Longue DurÈe and Revolution, Disciplines and Cultures.” Historia Mathematica, 29: 273.

[4] Kolmogorov, A. N., (1954). “On conservation of conditionally periodic motions for a small change in Hamilton’s function.,” Dokl. Akad. Nauk SSSR (N.S.), 98: 527–30.

[5] Lorenz, E. N. (1963). “Deterministic Nonperiodic Flow.” Journal of the Atmo- spheric Sciences 20(2): 130–41.

[6] Arnold,V.I.(1997).“From superpositions to KAM theory,”VladimirIgorevich Arnold. Selected, 60: 727–40.

[7] Moser, J. (1962). “On Invariant Curves of Area-Preserving Mappings of an Annulus.,” Nachr. Akad. Wiss. Göttingen Math.-Phys, Kl. II, 1–20.

[8] Arnold, V. I. (1963). “Small denominators and problems of the stability of motion in classical and celestial mechanics (in Russian),” Usp. Mat. Nauk., 18: 91–192,; Arnold, V. I. (1964). “Instability of Dynamical Systems with Many Degrees of Freedom.” Doklady Akademii Nauk Sssr 156(1): 9.

[9] Chirikov, B. V. (1969). Research concerning the theory of nonlinear resonance and stochasticity. Institute of Nuclear Physics, Novosibirsk. 4. Note: The Standard Map Jn+1 =Jn +εsinθn θn+1 =θn +Jn+1
is plotted in Fig. 3.31 in Nolte, Introduction to Modern Dynamics (2015) on p. 139. For small perturbation ε, two fixed points appear along the line J = 0 corresponding to p/q = 1: one is an elliptical point (with surrounding small orbits) and the other is a hyperbolic point where chaotic behavior is first observed. With increasing perturbation, q elliptical points and q hyperbolic points emerge for orbits with winding numbers p/q with small denominators (1/2, 1/3, 2/3 etc.). Other orbits with larger q are warped by the increasing perturbation but are not chaotic. These orbits reside on invariant tori, known as the KAM tori, that do not disintegrate into chaos at small perturbation. The set of KAM tori is a Cantor-like set with non- zero measure, ensuring that stable behavior can survive in the presence of perturbations, such as perturbation of the Earth’s orbit around the Sun by Jupiter. However, with increasing perturbation, orbits with successively larger values of q disintegrate into chaos. The last orbits to survive in the Standard Map are the golden mean orbits with p/q = φ–1 and p/q = 2–φ. The critical value of the perturbation required for the golden mean orbits to disintegrate into chaos is surprisingly large at εc = 0.97.

[10] Ruelle,D. and F.Takens (1971).“OntheNatureofTurbulence.”Communications in Mathematical Physics 20(3): 167–92.

[11] Gollub, J. P. and H. L. Swinney (1975). “Onset of Turbulence in a Rotating Fluid.” Physical Review Letters, 35(14): 927–30.

[12] May, R. M. (1976). “Simple Mathematical-Models with very complicated Dynamics.” Nature, 261(5560): 459–67.

[13] M. J. Feigenbaum, “Quantitative Universality for a Class of Nnon-linear Transformations,” Journal of Statistical Physics 19, 25-52 (1978).

[14] Packard, N.; Crutchfield, J. P.; Farmer, J. Doyne; Shaw, R. S. (1980). “Geometry from a Time Series”. Physical Review Letters. 45 (9): 712–716.

[15] Gleick,J.(1987).Chaos:MakingaNewScience,NewYork:Viking.p.180.

November 28, 2023November 28, 2023 by David D. Nolte

Fat Fractals, Arnold Tongues, and the Rings of Saturn

Fractals, those telescoping self-similar filigree meshes that marry mathematics and art, have become so mainstream, that they are even mentioned in the theme song of Disney’s 2013 mega-hit, Frozen.

My power flurries through the air into the ground
My soul is spiraling in frozen fractals all around
And one thought crystallizes like an icy blast
I’m never going back, the past is in the past
Let it Go, by Idina Menzel (Frozen, Disney 2013)

But not all fractals are cut from the same cloth. Some are thin and some are fat. The thin ones are the ones we know best, adorning the cover of books and magazines. But the fat ones may be more common and may play important roles, such as in the stability of celestial orbits in a many-planet neighborhood, or in the stability and structure of Saturn’s rings.

To get a handle on fat fractals, we will start with a familiar thin one, the zero-measure Cantor set.

The Zero-Measure Cantor Set

The famous one-third Cantor set is often the first fractal that you encounter in any introduction to fractals. (See my blog on a short history of fractals.) It lives on a one-dimensional line, and its iterative construction is intuitive and simple.

Start with a long thin bar of unit length. Then remove the middle third, leaving the endpoints. This leaves two identical bars of one-third length each. Next, remove the open middle third of each of these, again leaving the endpoints, leaving behind section pairs of one-nineth length. Then repeat ad infinitum. The points of the line that remain–all those segment endpoints–are the Cantor set.

Fig. 1 Construction of the 1/3 Cantor set by removing 1/3 segments at each level, and leaving the endpoints of each segment. The resulting set is a dust of points with a fractal dimension D = ln(2)/ln(3) = 0.6309.

The Cantor set has a fractal dimension that is easily calculated by noting that at each stage there are two elements (N = 2) that divided by three in size (b = 3). The fractal dimension is then

It is easy to prove that the collection of points of the Cantor set have no length because all of the length was removed.

For instance, at the first level, one third of the length was removed. At the second level, two segments of one-nineth length were removed. At the third level, four segments of one-twenty-sevength length were removed, and so on. Mathematically, this is

The infinite series in the brackets is a binomial series with the simple solution

Therefore, all the length has been removed, and none is left to the Cantor set, which is simply a collection of all the endpoints of all the segments that were removed.

The Cantor set is said to have a Lebesgue measure of zero. It behaves as a dust of isolated points.

A close relative of the Cantor set is the Sierpinski Carpet which is the two-dimensional analog. It begins with a square of unit side, then the middle third is removed (one nineth of the three-by-three array of square of one-third side), and so on.

*Fig. 2 A regular Sierpinski Carpet with fractal dimension D = ln(8)/ln(3) = 1.8928.*

The resulting Sierpinski Carpet has zero Lebesgue measure, just like the Cantor dust, because all the area has been removed.

There are also random Sierpinski Carpets as the sub-squares are removed from random locations.

*Fig. 3 A random Sierpinski Carpet with fractal dimension D = ln(8)/ln(3) = 1.8928.*

These fractals are “thin”, so-called because they are dusts with zero measure.

But the construction was constructed just so, such that the sum over all the removed sub-lengths summed to unity. What if less material had been taken at each step? What happens?

Fat Fractals

Instead of taking one-third of the original length, take instead one-fourth. But keep the one-third scaling level-to-level, as for the original Cantor Set.

*Fig. 4 A “fat” Cantor fractal constructed by removing 1/4 of a segment at each level instead of 1/3.*

The total length removed is

Therefore, three fourths of the length was removed, leaving behind one fourth of the material. Not only that, but the material left behind is contiguous—solid lengths. At each level, a little bit of the original bar remains, and still remains at the next level and the next. Therefore, it is said to have a Lebesgue measure of unity. This construction leads to a “fat” fractal.

*Fig. 5 Fat Cantor fractal showing the original Cantor 1/3 set (in black) and the extra contiguous segments (in red) that give the set a Lebesgue measure equal to one.*

Looking at Fig. 5, it is clear that the original Cantor dust is still present as the black segments interspersed among the red parts of the bar that are contiguous. But when two sets are added that have different “dimensions”, then the combined set has the larger dimension of the two, which is one-dimensional in this case. The fat Cantor set is one dimensional. One can still study its scaling properties, leading to another type of dimension known as an exterior measure [1], but where do such fat fractals occur? Why do they matter?

One answer is that they lie within the oddly named “Arnold Tongues” that arise in the study of synchronization and resonance connected to the stability of the solar system and the safety of its inhabitants.

Arnold Tongues

The study of synchronization explores and explains how two or more non-identical oscillators can lock themselves onto a common shared oscillation. For two systems to synchronize requires autonomous oscillators (like planetary orbits) with a period-dependent interaction (like gravity). Such interactions are “resonant” when the periods of the two orbits are integer ratios of each other, like 1:2 or 2:3. Such resonances ensure that there is a periodic forcing caused by the interaction that is some multiple of the orbital period. Think of tapping a rotating bicycle wheel twice per cycle or three times per cycle. Even if you are a little off in your timing, you can lock the tire rotation rate to a multiple of your tapping frequency. But if you are too far off on your timing, then the wheel will turn independently of your tapping.

Because rational ratios of integers are plentiful, there can be an intricate interplay between locked frequencies and unlocked frequencies. When the rotation rate is close to a resonance, then the wheel can frequency-lock to the tapping. Plotting the regions where the wheel synchronizes or not as a function of the frequency ratio and also as a function of the strength of the tapping leads to one of the iconic images of nonlinear dynamics: the Arnold tongue diagram.

Fig. 6 Arnold tongue diagram, showing the regions of frequency locking (black) at rational resonances as a function of coupling strength. At unity coupling strength, the set outside frequency-locked regions is fractal with D = 0.87. For all smaller coupling, a set along a horizontal is a fat fractal with topological dimension D = 1. The white regions are “ergodic”, as the phase of the oscillator runs through all possible values.

The Arnold tongues in Fig. 6 are the frequency locked regions (black) as a function of frequency ratio and coupling strength g. The black regions correspond to rational ratios of frequencies. For g = 1, the set outside frequency-locked regions (the white regions are “ergodic”, as the phase of the oscillator runs through all possible values) is a thin fractal with D = 0.87. For g < 1, the sets outside the frequency locked regions along a horizontal (at constant g) are fat fractals with topological dimension D = 1. For fat fractals, the fractal dimension is irrelevant, and another scaling exponent takes on central importance.

The Lebesgue measure μ of the ergodic regions (the regions that are not frequency locked) is a function of the coupling strength varying from μ = 1 at g = 0 to μ = 0 at g = 1. When the pattern is coarse-grained at a scale ε, then the scaling of a fat fractal is

where β is the scaling exponent that characterizes the fat fractal.

From numerical studies [2] there is strong evidence that β = 2/3 for the fat fractals of Arnold Tongues.

The Rings of Saturn

Arnold Tongues arise in KAM theory on the stability of the solar system (See my blog on KAM and how number theory protects us from the chaos of the cosmos). Fortunately, Jupiter is the largest perturbation to Earth’s orbit, but its influence, while non-zero, is not enough to seriously affect our stability. However, there is a part of the solar system where rational resonances are not only large but dominant: Saturn’s rings.

Saturn’s rings are composed of dust and ice particles that orbit Saturn with a range of orbital periods. When one of these periods is a rational fraction of the orbital period of a moon, then a resonance condition is satisfied. Saturn has many moons, producing highly corrugated patterns in Saturn’s rings at rational resonances of the periods.

Fig. 7 A close up of Saturn’s rings shows a highly detailed set of bands. Particles at a given radius have a given period (set by Kepler’s third law). When the period of dust particles in the ring are an integer ratio of the period of a “shepherd moon”, then a resonance can drive density rings. [See image reference.]

The moons Janus and Epithemeus share an orbit around Saturn in a rare 1:1 resonance in which they swap positions every four years. Their combined gravity excites density ripples in Saturn’s rings, photographed by the Cassini spacecraft and shown in Fig. 8.

*Fig. 8 Cassini spacecraft photograph of density ripples in Saturns rings caused by orbital resonance with the pair of moons Janus and Epithemeus.*

One Canadian astronomy group converted the resonances of the moon Janus into a musical score to commenorate Cassini’s final dive into the planet Saturn in 2017. The Janus resonances are shown in Fig. 9 against the pattern of Saturn’s rings.

*Fig. 7 Rational resonances for subrings of Saturn relative to its moon Janus.*

Saturn’s rings, orbital resonances, Arnold tongues and fat fractals provide a beautiful example of the power of dynamics to create structure, and the primary role that structure plays in deciphering the physics of complex systems.

References:

[1] C. Grebogi, S. W. McDonald, E. Ott, and J. A. Yorke, “EXTERIOR DIMENSION OF FAT FRACTALS,” Physics Letters A 110, 1-4 (1985).

[2] R. E. Ecke, J. D. Farmer, and D. K. Umberger, “Scaling of the Arnold tongues,” Nonlinearity 2, 175-196 (1989).

September 25, 2022November 10, 2023 by David D. Nolte

Is There a Quantum Trajectory? The Phase-Space Perspective

At the dawn of quantum theory, Heisenberg, Schrödinger, Bohr and Pauli were embroiled in a dispute over whether trajectories of particles, defined by their positions over time, could exist. The argument against trajectories was based on an apparent paradox: To draw a “line” depicting a trajectory of a particle along a path implies that there is a momentum vector that carries the particle along that path. But a line is a one-dimensional curve through space, and since at any point in time the particle’s position is perfectly localized, then by Heisenberg’s uncertainty principle, it can have no definable momentum to carry it along.

My previous blog shows the way out of this paradox, by assembling wavepackets that are spread in both space and momentum, explicitly obeying the uncertainty principle. This is nothing new to anyone who has taken a quantum course. But the surprising thing is that in some potentials, like a harmonic potential, the wavepacket travels without broadening, just like classical particles on a trajectory. A dramatic demonstration of this can be seen in this YouTube video. But other potentials “break up” the wavepacket, especially potentials that display classical chaos. Because phase space is one of the best tools for studying classical chaos, especially Hamiltonian chaos, it can be enlisted to dig deeper into the question of the quantum trajectory—not just about the existence of a quantum trajectory, but why quantum systems retain a shadow of their classical counterparts.

Phase Space

Phase space is the state space of Hamiltonian systems. Concepts of phase space were first developed by Boltzmann as he worked on the problem of statistical mechanics. Phase space was later codified by Gibbs for statistical mechanics and by Poincare for orbital mechanics, and it was finally given its name by Paul and Tatiana Ehrenfest (a husband-wife team) in correspondence with the German physicist Paul Hertz (See Chapter 6, “The Tangled Tale of Phase Space”, in Galileo Unbound by D. D. Nolte (Oxford, 2018)).

The stretched-out phase-space functions … are very similar to the stochastic layer that forms in separatrix chaos in classical systems.

The idea of phase space is very simple for classical systems: it is just a plot of the momentum of a particle as a function of its position. For a given initial condition, the trajectory of a particle through its natural configuration space (for instance our 3D world) is traced out as a path through phase space. Because there is one momentum variable per degree of freedom, then the dimensionality of phase space for a particle in 3D is 6D, which is difficult to visualize. But for a one-dimensional dynamical system, like a simple harmonic oscillator (SHO) oscillating in a line, the phase space is just two-dimensional, which is easy to see. The phase-space trajectories of an SHO are simply ellipses, and if the momentum axis is scaled appropriately, the trajectories are circles. The particle trajectory in phase space can be animated just like a trajectory through configuration space as the position and momentum change in time p(x(t)). For the SHO, the point follows the path of a circle going clockwise.

*Fig. 1 Phase space of the simple harmonic oscillator. The “orbits” have constant energy.*

A more interesting phase space is for the simple pendulum, shown in Fig. 2. There are two types of orbits: open and closed. The closed orbits near the origin are like those of a SHO. The open orbits are when the pendulum is spinning around. The dividing line between the open and closed orbits is called a separatrix. Where the separatrix intersects itself is a saddle point. This saddle point is the most important part of the phase space portrait: it is where chaos emerges when perturbations are added.

Fig. 2 Phase space for a simple pendulum. For small amplitudes the orbits are closed like those of a SHO. For large amplitudes the orbits become open as the pendulum spins about its axis. (Reproduced from Introduction to Modern Dynamics, 2nd Ed., pg. )

One route to classical chaos is through what is known as “separatrix chaos”. It is easy to see why saddle points (also known as hyperbolic points) are the source of chaos: as the system trajectory approaches the saddle, it has two options of which directions to go. Any additional degree of freedom in the system (like a harmonic drive) can make the system go one way on one approach, and the other way on another approach, mixing up the trajectories. An example of the stochastic layer of separatrix chaos is shown in Fig. 3 for a damped driven pendulum. The chaotic behavior that originates at the saddle point extends out along the entire separatrix.

*Fig. 3 The stochastic layer of separatrix chaos for a damped driven pendulum. (Reproduced from Introduction to Modern Dynamics, 2nd Ed., pg. )*

The main question about whether or not there is a quantum trajectory depends on how quantum packets behave as they approach a saddle point in phase space. Since packets are spread out, it would be reasonable to assume that parts of the packet will go one way, and parts of the packet will go another. But first, one has to ask: Is a phase-space description of quantum systems even possible?

Quantum Phase Space: The Wigner Distribution Function

Phase-space portraits are arguably the most powerful tool in the toolbox of classical dynamics, and one would like to retain its uses for quantum systems. However, there is that pesky paradox about quantum trajectories that cannot admit the existence of one-dimensional curves through such a phase space. Furthermore, there is no direct way of taking a wavefunction and simply “finding” its position or momentum to plot points on such a quantum phase space.

The answer was found in 1932 by Eugene Wigner (1902 – 1905), an Hungarian physicist working at Princeton. He realized that it was impossible to construct a quantum probability distribution in phase space that had positive values everywhere. This is a problem, because negative probabilities have no direct interpretation. But Wigner showed that if one relaxed the requirements a bit, so that expectation values computed over some distribution function (that had positive and negative values) gave correct answers that matched experiments, then this distribution function would “stand in” for an actual probability distribution.

The distribution function that Wigner found is called the Wigner distribution function. Given a wavefunction ψ(x), the Wigner distribution is defined as

*Fig. 4 Wigner distribution function in (x, p) phase space.*

The Wigner distribution function is the Fourier transform of the convolution of the wavefunction. The pure position dependence of the wavefunction is converted into a spread-out position-momentum function in phase space. For a Gaussian wavefunction ψ(x) with a finite width in space, the W-function in phase space is a two-dimensional Gaussian with finite widths in both space and momentum. In fact, the Δx-Δp product of the W-function is precisely the uncertainty production of the Heisenberg uncertainty relation.

The question of the quantum trajectory from the phase-space perspective becomes whether a Wigner function behaves like a localized “packet” that evolves in phase space in a way analogous to a classical particle, and whether classical chaos is reflected in the behavior of quantum systems.

The Harmonic Oscillator

The quantum harmonic oscillator is a rare and special case among quantum potentials, because the energy spacings between all successive states are all the same. This makes it possible for a Gaussian wavefunction, which is a superposition of the eigenstates of the harmonic oscillator, to propagate through the potential without broadening. To see an example of this, watch the first example in this YouTube video for a Schrödinger cat state in a two-dimensional harmonic potential. For this very special potential, the Wigner distribution behaves just like a (broadened) particle on an orbit in phase space, executing nice circular orbits.

A comparison of the classical phase-space portrait versus the quantum phase-space portrait is shown in Fig. 5. Where the classical particle is a point on an orbit, the quantum particle is spread out, obeying the Δx-Δp Heisenberg product, but following the same orbit as the classical particle.

Fig. 5 Classical versus quantum phase-space portraits for a harmonic oscillator. For a classical particle, the trajectory is a point executing an orbit. For a quantum particle, the trajectory is a Wigner distribution that follows the same orbit as the classical particle.

However, a significant new feature appears in the Wigner representation in phase space when there is a coherent superposition of two states, known as a “cat” state, after Schrödinger’s cat. This new feature has no classical analog. It is the coherent interference pattern that appears at the zero-point of the harmonic oscillator for the Schrödinger cat state. There is no such thing as “classical” coherence, so this feature is absent in classical phase space portraits.

Two examples of Wigner distributions are shown in Fig. 6 for a statistical (incoherent) mixture of packets and a coherent superposition of packets. The quantum coherence signature is present in the coherent case but not the statistical mixture case. The coherence in the Wigner distribution represents “off-diagonal” terms in the density matrix that leads to interference effects in quantum systems. Quantum computing algorithms depend critically on such coherences that tend to decay rapidly in real-world physical systems, known as decoherence, and it is possible to make statements about decoherence by watching the zero-point interference.

Fig. 6 Quantum phase-space portraits of double wave packets. On the left, the wave packets have no coherence, being a statistical mixture. On the right is the case for a coherent superposition, or “cat state” for two wave packets in a one-dimensional harmonic oscillator.

Whereas Gaussian wave packets in the quantum harmonic potential behave nearly like classical systems, and their phase-space portraits are almost identical to the classical phase-space view (except for the quantum coherence), most quantum potentials cause wave packets to disperse. And when saddle points are present in the classical case, then we are back to the question about how quantum packets behave as they approach a saddle point in phase space.

Quantum Pendulum and Separatrix Chaos

One of the simplest anharmonic oscillators is the simple pendulum. In the classical case, the period diverges if the pendulum gets very close to going vertical. A similar thing happens in the quantum case, but because the motion has strong anharmonicity, an initial wave packet tends to spread dramatically as parts of the wavefunction less vertical stretch away from the part of the wave function that is more nearly vertical. Fig. 7 is a snap-shot about a eighth of a period after the wave packet was launched. The packet has already stretched out along the separatrix. A double-cat-state was used, so there is a second packet that has coherent interference with the first. To see a movie of the time evolution of the wave packet and the orbit in quantum phase space, see the YouTube video.

Fig. 7 Wavefunction of a quantum pendulum released near vertical. The phase-space portrait is very similar to the classical case, except that the phase-space distribution is stretched out along the separatrix. The initial state for the phase-space portrait was a cat state.

The simple pendulum does have a saddle point, but it is degenerate because the angle is modulo -2-pi. A simple potential that has a non-degenerate saddle point is a double-well potential.

Quantum Double-Well and Separatrix Chaos

The symmetric double-well potential has a saddle point at the mid-point between the two well minima. A wave packet approaching the saddle will split into to packets that will follow the individual separatrixes that emerge from the saddle point (the unstable manifolds). This effect is seen most dramatically in the middle pane of Fig. 8. For the full video of the quantum phase-space evolution, see this YouTube video. The stretched-out distribution in phase space is highly analogous to the separatrix chaos seen for the classical system.

Fig. 8 Phase-space portraits of the Wigner distribution for a wavepacket in a double-well potential. The packet approaches the central saddle point, where the probability density splits along the unstable manifolds.

Conclusion

A common statement often made about quantum chaos is that quantum systems tend to suppress chaos, only exhibiting chaos for special types of orbits that produce quantum scars. However, from the phase-space perspective, the opposite may be true. The stretched-out Wigner distribution functions, for critical wave packets that interact with a saddle point, are very similar to the stochastic layer that forms in separatrix chaos in classical systems. In this sense, the phase-space description brings out the similarity between classical chaos and quantum chaos.

By David D. Nolte Sept. 25, 2022

YouTube Video

For more on the history of quantum trajectories, see Galileo Unbound from Oxford Press:

Order from Amazon

References

1. T. Curtright, D. Fairlie, C. Zachos, A Concise Treatise on Quantum Mechanics in Phase Space. (World Scientific, New Jersey, 2014).

2. J. R. Nagel, A Review and Application of the Finite-Difference Time-Domain Algorithm Applied to the Schrödinger Equation, ACES Journal, Vol. 24, NO. 1, pp. 1-8 (2009)

September 4, 2022April 20, 2023 by David D. Nolte

Is There a Quantum Trajectory?

Heisenberg’s uncertainty principle is a law of physics – it cannot be violated under any circumstances, no matter how much we may want it to yield or how hard we try to bend it. Heisenberg, as he developed his ideas after his lone epiphany like a monk on the isolated island of Helgoland off the north coast of Germany in 1925, became a bit of a zealot, like a religious convert, convinced that all we can say about reality is a measurement outcome. In his view, there was no independent existence of an electron other than what emerged from a measuring apparatus. Reality, to Heisenberg, was just a list of numbers in a spread sheet—matrix elements. He took this line of reasoning so far that he stated without exception that there could be no such thing as a trajectory in a quantum system. When the great battle commenced between Heisenberg’s matrix mechanics against Schrödinger’s wave mechanics, Heisenberg was relentless, denying any reality to Schrödinger’s wavefunction other than as a calculation tool. He was so strident that even Bohr, who was on Heisenberg’s side in the argument, advised Heisenberg to relent [1]. Eventually a compromise was struck, as Heisenberg’s uncertainty principle allowed Schrödinger’s wave functions to exist within limits—his uncertainty limits.

Disaster in the Poconos

Yet the idea of an actual trajectory of a quantum particle remained a type of heresy within the close quantum circles. Years later in 1948, when a young Richard Feynman took the stage at a conference in the Poconos, he almost sabotaged his career in front of Bohr and Dirac—two of the giants who had invented quantum mechanics—by having the audacity to talk about particle trajectories in spacetime diagrams.

Feynman was making his first presentation of a new approach to quantum mechanics that he had developed based on path integrals. The challenge was that his method relied on space-time graphs in which “unphysical” things were allowed to occur. In fact, unphysical things were required to occur, as part of the sum over many histories of his path integrals. For instance, a key element in the approach was allowing electrons to travel backwards in time as positrons, or a process in which the electron and positron annihilate into a single photon, and then the photon decays back into an electron-positron pair—a process that is not allowed by mass and energy conservation. But this is a possible history that must be added to Feynman’s sum.

It all looked like nonsense to the audience, and the talk quickly derailed. Dirac pestered him with questions that he tried to deflect, but Dirac persisted like a raven. A question was raised about the Pauli exclusion principle, about whether an orbital could have three electrons instead of the required two, and Feynman said that it could—all histories were possible and had to be summed over—an answer that dismayed the audience. Finally, as Feynman was drawing another of his space-time graphs showing electrons as lines, Bohr rose to his feet and asked derisively whether Feynman had forgotten Heisenberg’s uncertainty principle that made it impossible to even talk about an electron trajectory.

It was hopeless. The audience gave up and so did Feynman as the talk just fizzled out. It was a disaster. What had been meant to be Feynman’s crowning achievement and his entry to the highest levels of theoretical physics, had been a terrible embarrassment. He slunk home to Cornell where he sank into one of his depressions. At the close of the Pocono conference, Oppenheimer, the reigning king of physics, former head of the successful Manhattan Project and newly selected to head the prestigious Institute for Advanced Study at Princeton, had been thoroughly disappointed by Feynman.

But what Bohr and Dirac and Oppenheimer had failed to understand was that as long as the duration of unphysical processes was shorter than the energy differences involved, then it was literally obeying Heisenberg’s uncertainty principle. Furthermore, Feynman’s trajectories—what became his famous “Feynman Diagrams”—were meant to be merely cartoons—a shorthand way to keep track of lots of different contributions to a scattering process. The quantum processes certainly took place in space and time, conceptually like a trajectory, but only so far as time durations, and energy differences and locations and momentum changes were all within the bounds of the uncertainty principle. Feynman had invented a bold new tool for quantum field theory, able to supply deep results quickly. But no one at the Poconos could see it.

Coherent States

When Feynman had failed so miserably at the Pocono conference, he had taken the stage after Julian Schwinger, who had dazzled everyone with his perfectly scripted presentation of quantum field theory—the competing theory to Feynman’s. Schwinger emerged the clear winner of the contest. At that time, Roy Glauber (1925 – 2018) was a young physicist just taking his PhD from Schwinger at Harvard, and he later received a post-doc position at Princeton’s Institute for Advanced Study where he became part of a miniature revolution in quantum field theory that revolved around—not Schwinger’s difficult mathematics—but Feynman’s diagrammatic method. So Feynman won in the end. Glauber then went on to Caltech, where he filled in for Feynman’s lectures when Feynman was off in Brazil playing the bongos. Glauber eventually returned to Harvard where he was already thinking about the quantum aspects of photons in 1956 when news of the photon correlations in the Hanbury-Brown Twiss (HBT) experiment were published. Three years later, when the laser was invented, he began developing a theory of photon correlations in laser light that he suspected would be fundamentally different than in natural chaotic light.

Because of his background in quantum field theory, and especially quantum electrodynamics, it was fairly easy to couch the quantum optical properties of coherent light in terms of Dirac’s creation and annihilation operators of the electromagnetic field. Glauber developed a “coherent state” operator that was a minimum uncertainty state of the quantized electromagnetic field, related to the minimum-uncertainty wave functions derived initially by Schrödinger in the late 1920’s. The coherent state represents a laser operating well above the lasing threshold and behaved as “the most classical” wavepacket that can be constructed. Glauber was awarded the Nobel Prize in Physics in 2005 for his work on such “Glauber states” in quantum optics.

Quantum Trajectories

Glauber’s coherent states are built up from the natural modes of a harmonic oscillator. Therefore, it should come as no surprise that these coherent-state wavefunctions in a harmonic potential behave just like classical particles with well-defined trajectories. The quadratic potential matches the quadratic argument of the the Gaussian wavepacket, and the pulses propagate within the potential without broadening, as in Fig. 3, showing a snapshot of two wavepackets propagating in a two-dimensional harmonic potential. This is a somewhat radical situation, because most wavepackets in most potentials (or even in free space) broaden as they propagate. The quadratic potential is a special case that is generally not representative of how quantum systems behave.

Fig. 3 Harmonic potential in 2D and two examples of pairs of pulses propagating without broadening. The wavepackets in the center are oscillating in line, and the wavepackets on the right are orbiting the center of the potential in opposite directions. (Movies of the quantum trajectories can be viewed at Physics Unbound.)

To illustrate this special status for the quadratic potential, the wavepackets can be launched in a potential with a quartic perturbation. The quartic potential is anharmonic—the frequency of oscillation depends on the amplitude of oscillation unlike for the harmonic oscillator, where amplitude and frequency are independent. The quartic potential is integrable, like the harmonic oscillator, and there is no avenue for chaos in the classical analog. Nonetheless, wavepackets broaden as they propagate in the quartic potential, eventually spread out into a ring in the configuration space, as in Fig. 4.

Fig. 4 Potential with a quartic corrections. The initial gaussian pulses spread into a “ring” orbiting the center of the potential.

A potential with integrability has as many conserved quantities to the motion as there are degrees of freedom. Because the quartic potential is integrable, the quantum wavefunction may spread, but it remains highly regular, as in the “ring” that eventually forms over time. However, integrable potentials are the exception rather than the rule. Most potentials lead to nonintegrable motion that opens the door to chaos.

A classic (and classical) potential that exhibits chaos in a two-dimensional configuration space is the famous Henon-Heiles potential. This has a four-dimensional phase space which admits classical chaos. The potential has a three-fold symmetry which is one reason it is non-integral, since a particle must “decide” which way to go when it approaches a saddle point. In the quantum regime, wavepackets face the same decision, leading to a breakup of the wavepacket on top of a general broadening. This allows the wavefunction eventually to distribute across the entire configuration space, as in Fig. 5.

Fig. 5 The Henon-Heiles two-dimensional potential supports Hamiltonian chaos in the classical regime. In the quantum regime, the wavefunction spreads to eventually fill the accessible configuration space (for constant energy).

Youtube Video

Movies of quantum trajectories can be viewed at my Youtube Channel, Physics Unbound. The answer to the question “Is there a quantum trajectory?” can be seen visually as the movies run—they do exist in a very clear sense under special conditions, especially coherent states in a harmonic oscillator. And the concept of a quantum trajectory also carries over from a classical trajectory in cases when the classical motion is integrable, even in cases when the wavefunction spreads over time. However, for classical systems that display chaotic motion, wavefunctions that begin as coherent states break up into chaotic wavefunctions that fill the accessible configuration space for a given energy. The character of quantum evolution of coherent states—the most classical of quantum wavefunctions—in these cases reflects the underlying character of chaotic motion in the classical analogs. This process can be seen directly watching the movies as a wavepacket approaches a saddle point in the potential and is split. Successive splits of the multiple wavepackets as they interact with the saddle points is what eventually distributes the full wavefunction into its chaotic form.

Therefore, the idea of a “quantum trajectory”, so thoroughly dismissed by Heisenberg, remains a phenomenological guide that can help give insight into the behavior of quantum systems—both integrable and chaotic.

As a side note, the laws of quantum physics obey time-reversal symmetry just as the classical equations do. In the third movie of “A Quantum Ballet“, wavefunctions in a double-well potential are tracked in time as they start from coherent states that break up into chaotic wavefunctions. It is like watching entropy in action as an ordered state devolves into a disordered state. But at the half-way point of the movie, the imaginary part of the wavefunction has its sign flipped, and the dynamics continue. But now the wavefunctions move from disorder into an ordered state, seemingly going against the second law of thermodynamics. Flipping the sign of the imaginary part of the wavefunction at just one instant in time plays the role of a time-reversal operation, and there is no violation of the second law.

By David D. Nolte, Sept. 4, 2022

YouTube Video

YouTube Video of Quantum Trajectories

For more on the history of quantum trajectories, see Galileo Unbound from Oxford Press:

Order from Amazon

References

[1] See Chapter 8 , On the Quantum Footpath, in Galileo Unbound, D. D. Nolte (Oxford University Press, 2018)

[2] J. R. Nagel, A Review and Application of the Finite-Difference Time-Domain Algorithm Applied to the Schrödinger Equation, ACES Journal, Vol. 24, NO. 1, pp. 1-8 (2009)

August 14, 2022November 10, 2023 by David D. Nolte

Quantum Chaos and the Cheshire Cat

Alice’s disturbing adventures in Wonderland tumbled upon her like a string of accidents as she wandered a world of chaos. Rules were never what they seemed and shifted whenever they wanted. She even met a cat who grinned ear-to-ear and could disappear entirely, or almost entirely, leaving only its grin.

The vanishing Cheshire Cat reminds us of another famous cat—Arnold’s Cat—that introduced the ideas of stretching and folding of phase-space volumes in non-integrable Hamiltonian systems. But when Arnold’s Cat becomes a Quantum Cat, a central question remains: What happens to the chaotic behavior of the classical system … does it survive the transition to quantum mechanics? The answer is surprisingly like the grin of the Cheshire Cat—the cat vanishes, but the grin remains. In the quantum world of the Cheshire Cat, the grin of the classical cat remains even after the rest of the cat vanished.

The Cheshire Cat fades away, leaving only its grin, like a fine filament, as classical chaos fades into quantum, leaving behind a quantum scar.

The Quantum Mechanics of Classically Chaotic Systems

The simplest Hamiltonian systems are integrable—they have as many constants of the motion as degrees of freedom. This holds for quantum systems as well as for classical. There is also a strong correspondence between classical and quantum systems for the integrable cases—literally the Correspondence Principle—that states that quantum systems at high quantum number approach classical behavior. Even at low quantum numbers, classical resonances are mirrored by quantum eigenfrequencies that can show highly regular spectra.

But integrable systems are rare—surprisingly rare. Almost no real-world Hamiltonian system is integrable, because the real world warps the ideal. No spring can displace indefinitely, and no potential is perfectly quadratic. There are always real-world non-idealities that destroy one constant of the motion or another, opening the door to chaos.

When classical Hamiltonian systems become chaotic, they don’t do it suddenly. Almost all transitions to chaos in Hamiltonian systems are gradual. One of the best examples of this is the KAM theory that starts with invariant action integrals that generate invariant tori in phase space. As nonintegrable perturbations increase, the tori break up slowly into island chains of stability as chaos infiltrates the separatrixes—first as thin filaments of chaos surrounding the islands—then growing in width to take up more and more of phase space. Even when chaos is fully developed, small islands of stability can remain—the remnants of stable orbits of the unperturbed system.

When the classical becomes quantum, chaos softens. Quantum wave functions don’t like to be confined—they spread and they tunnel. The separatrix of classical chaos—that barrier between regions of phase space—cannot constrain the exponential tails of wave functions. And the origin of chaos itself—the homoclinic point of the separatrix—gets washed out. Then the regular orbits of the classical system reassert themselves, and they appear, like the vestige of the Cheshire Cat, as a grin.

The Quantum Circus

The empty stadium is a surprisingly rich dynamical system that has unexpected structure in both the classical and the quantum domain. Its importance in classical dynamics comes from the fact that its periodic orbits are unstable and its non-periodic orbits are ergodic (filling all available space if given long enough). The stadium itself is empty so that particles (classical or quantum) are free to propagate between reflections from the perfectly-reflecting walls of the stadium. The ergodicity comes from the fact that the stadium—like a classic Roman chariot-race stadium, also known as a circus—is not a circle, but has a straight stretch between two half circles. This simple modification takes the stable orbits of the circle into the unstable orbits of the stadium.

A single classical orbit in a stadium is shown in Fig 1. This is an ergodic orbit that is non-periodic and eventually would fill the entire stadium space. There are other orbits that are nearly periodic, such as one that bounces back and forth vertically between the linear portions, but even this orbit will eventually wander into the circular part of the stadium and then become ergodic. The big quantum-classical question is what happens to these classical orbits when the stadium is shrunk to the nanoscale?

Fig. 1 A classical trajectory in a stadium. It will eventually visit every point, a property known as ergodicity.

Simulating an evolving quantum wavefunction in free space is surprisingly simple. Given a beginning quantum wavefunction A(x,y,t₀), the discrete update equation is

Perfect reflection from the boundaries of the stadium are incorporated through imposing a boundary condition that sends the wavefunction to zero. Simple!

A snap-shot of a wavefunction evolving in the stadium is shown in Fig. 2. To see a movie of the time evolution, see my YouTube episode.

Fig. 2 Snapshot of a quantum wavefunction in the stadium. (From YouTube)

The time average of the wavefunction after a long time has passed is shown in Fig. 3. Other than the horizontal nodal line down the center of the stadium, there is little discernible structure or symmetry. This is also true for the mean squared wavefunction shown in Fig. 4, although there is some structure that may be emerging in the semi-circular regions.

Fig. 3 Time-average wavefunction after a long time.

Fig. 4 Time-average of the squared wavefunction after a long time.

On the other hand, for special initial conditions that have a lot of symmetry, something remarkable happens. Fig. 5 shows several mean-squared results for special initial conditions. There is definite structure in these cases that were given the somewhat ugly name “quantum scars” in the 1980’s by Eric Heller who was one of the first to study this phenomenon [1].

Fig. 5 Quantum scars reflect periodic (but unstable) orbits of the classical system. Quantum effects tend to quench chaos and favor regular motion.

One can superpose highly-symmetric classical trajectories onto the figures, as shown in the bottom row. All of these classical orbits go through a high-symmetry point, such as the center of the stadium (on the left image) and through the focal point of the circular mirrors (in the other two images). The astonishing conclusion of this exercise is that the highly-symmetric periodic classical orbits remain behind as quantum scars—like the Cheshire Cat’s grin—when the system is in the quantum realm. The classical orbits that produce quantum scars have the important property of being periodic but unstable. A slight perturbation from the symmetric trajectory causes it to eventually become ergodic (chaotic). These scars are regions with enhanced probability density, what might be termed “quantum trajectories”, but do not show strong interference patterns.

It is important to make the distinction that it is also possible to construct special wavefunctions that are strictly periodic, such as a wave bouncing perfectly vertically between the straight portions. This leads to large-scale interference patterns that are not the same as the quantum scars.

Quantum Chaos versus Laser Speckle

In addition to the bouncing-wave cases that do not strictly produce quantum scars, there is another “neutral” phenomenon that produces interference patterns that look a lot like scars, but are simply the random addition of lots of plane waves with the same wavelength [2]. A snapshot in time of one of these superpositions is shown in Fig. 6. To see how the waves add together, see the YouTube channel episode.

Fig. 6 The sum of 100 randomly oriented plane waves of constant wavelength. (A snapshot from YouTube.)

By David D. Nolte, Aug. 14, 2022

YouTube Video

References

[1] Heller E J, Bound-state eigenfunctions of classically chaotic hamiltonian-systems – scars of periodic-orbits, Physical Review Letters 53 ,1515 (1984)

[2] Gutzwiller M C, Chaos in classical and quantum mechanics (New York: New York : Springer-Verlag, 1990)

February 28, 2022June 1, 2023 by David D. Nolte

Life in a Solar System with a Super-sized Jupiter

There are many known super-Jupiters that orbit their stars—they are detected through a slight Doppler wobble they induce on their stars [1]. But what would become of a rocky planet also orbiting those stars as they feel the tug of both the star and the super planet?

This is not of immediate concern for us, because our solar system has had its current configuration of planets for over 4 billion years. But there can be wandering interstellar planets or brown dwarfs that could visit our solar system, like Oumuamua did in 2017, but much bigger and able to scramble the planetary orbits. Such hypothesized astronomical objects have been given the name “Nemesis“, and it warrants thought on what living in an altered solar system might be like.

What would happen to Earth if Jupiter were 50 times bigger? Could we survive?

The Three-Body Problem

The Sun-Earth-Jupiter configuration is a three-body problem that has a long and interesting history, playing a key role in several aspects of modern dynamics [2]. There is no general analytical solution to the three-body problem. To find the behavior of three mutually interacting bodies requires numerical solution. However, there are subsets of the three-body problem that do yield to partial analytical approaches. One of these is called the restricted three-body problem [3]. It consists of two massive bodies plus a third (nearly) massless body that all move in a plane. This restricted problem was first tackled by Euler and later by Poincaré, who discovered the existence of chaos in its solutions.

The geometry of the restricted three-body problem is shown in Fig. 1. In this problem, take mass m₁ = m_S to be the Sun’s mass, m₂ = m_J to be Jupiter’s mass, and the third (small) mass is the Earth.

Fig. 1 The restricted 3-body problem in the plane. The third mass is negligible relative to the first two masses that obey 2-body dynamics.

The equation of motion for the Earth is

where

and the parameter ξ characterizes the strength of the perturbation of the Earth’s orbit around the Sun. The parameters for the Jupiter-Sun system are

with

for the 11.86 year journey of Jupiter around the Sun. Eq. (1) is a four-dimensional non-autonomous flow

The solutions of an Earth orbit are shown in Fig.2. The natural Earth-Sun-Jupiter system has a mass ratio m_J/m_S = 0.001 for Jupiter relative to the Sun mass. Even in this case, Jupiter causes perturbations of the Earth’s orbit by about one percent. If the mass of Jupiter increases, the perturbations would grow larger until around ξ= 0.06 when the perturbations become severe and the orbit grows unstable. The Earth gains energy from the momentum of the Sun-Jupiter system and can reach escape velocity. The simulation for a mass ratio of 0.07 shows the Earth ejected from the Solar System.

Fig.2 Orbit of Earth as a function of the size of a Jupiter-like planet. The natural system has a Jupiter-Earth mass ratio of 0.03. As the size of Jupiter increases, the Earth orbit becomes unstable and can acquire escape velocity to escape from the Solar System. From body3.m. (Reprinted from Ref. [4])

The chances for ejection depends on initial conditions for these simulations, but generally the danger becomes severe when Jupiter is about 50 times larger than it currently is. Otherwise the Earth remains safe from ejection. However, if the Earth is to keep its climate intact, then Jupiter should not be any larger than about 5 times its current size. At the other extreme, for a planet 70 times larger than Jupiter, the Earth may not get ejected at once, but it can take a wild ride through the solar system. A simulation for a 70x Jupiter is shown in Fig. 3. In this case, the Earth is captured for a while as a “moon” of Jupiter in a very tight orbit around the super planet as it orbits the sun before it is set free again to orbit the sun in highly elliptical orbits. Because of the premise of the restricted three-body problem, the Earth has no effect on the orbit of Jupiter.

Fig. 3 Orbit of Earth for T_J = 11.86 years and ξ = 0.069. The radius of Jupiter is R_J = 5.2. Earth is “captured” for a while by Jupiter into a very tight orbit.

Resonance

If Nemesis were to swing by and scramble the solar system, then Jupiter might move closer to the Earth. More ominously, the period of Jupiter’s orbit could come into resonance with the Earth’s period. This occurs when the ratio of orbital periods is a ratio of small integers. Resonance can amplify small perturbations, so perhaps Jupiter would become a danger to Earth. However, the forces exerted by Jupiter on the Earth changes the Earth’s orbit and hence its period, preventing strict resonance to occur, and the Earth is not ejected from the solar system even for initial rational periods or larger planet mass. This is related to the famous KAM theory of resonances by Kolmogorov, Arnold and Moser that tends to protect the Earth from the chaos of the solar system. More often than not in these scenarios, the Earth is either captured by the super Jupiter, or it is thrown into a large orbit that is still bound to the sun. Some examples are given in the following figures.

Fig. 5 Earth orbit for T_J = 12 years and ξ = 0.071. The Earth is thrown into a nearly circular orbit beyond the orbit of Saturn.

Fig. 6 Earth Orbit for T_J = 4 years and ξ = 0.0615. Earth is thrown into an orbit of high ellipticity out to the orbit of Neptune.

Life on a planet in a solar system with two large bodies has been envisioned in dramatic detail in the science fiction novel “Three-Body Problem” by Liu Cixin about the Trisolarians of the closest known exoplanet to Earth–Proxima Centauri b.

By David D. Nolte, Feb. 28, 2022

Matlab Code: body3.m

function body3

clear

chsi0 = 1/1000;     % Earth-moon ratio = 1/317
wj0 = 2*pi/11.86;

wj = 2*pi/8;
chsi = 73*chsi0;    % (11.86,60) (11.86,67.5) (11.86,69) (11.86,70) (4,60) (4,61.5) (8,73) (12,71) 

rj = 5.203*(wj0/wj)^0.6666

rsun = chsi*rj/(1+chsi);
rjup = (1/chsi)*rj/(1+1/chsi);

r0 = 1-rsun;
y0 = [r0 0 0 2*pi/sqrt(r0)];

tspan = [0 300];
options = odeset('RelTol',1e-5,'AbsTol',1e-6);
[t,y] = ode45(@f5,tspan,y0,options);

figure(1)
plot(t,y(:,1),t,y(:,3))

figure(2)
plot(y(:,1),y(:,3),'k')
axis equal
axis([-6 6 -6 6])

RE = sqrt(y(:,1).^2 + y(:,3).^2);
stdRE = std(RE)

%print -dtiff -r800 threebody

    function yd = f5(t,y)
        
        xj = rjup*cos(wj*t);
        yj = rjup*sin(wj*t);
        xs = -rsun*cos(wj*t);
        ys = -rsun*sin(wj*t);
        rj32 = ((y(1) - xj).^2 + (y(3) - yj).^2).^1.5;
        r32 = ((y(1) - xs).^2 + (y(3) - ys).^2).^1.5;

        yp(1) = y(2);
        yp(2) = -4*pi^2*((y(1)-xs)/r32 + chsi*(y(1)-xj)/rj32);
        yp(3) = y(4);
        yp(4) = -4*pi^2*((y(3)-ys)/r32 + chsi*(y(3)-yj)/rj32);
 
        yd = [yp(1);yp(2);yp(3);yp(4)];

    end     % end f5

end

References:

[1] D. D. Nolte, “The Fall and Rise of the Doppler Effect,” Physics Today, vol. 73, no. 3, pp. 31-35, Mar (2020)

[2] J. Barrow-Green, Poincaré and the three body problem. London Mathematical Society, 1997.

[3] M. C. Gutzwiller, “Moon-Earth-Sun: The oldest three-body problem,” Reviews of Modern Physics, vol. 70, no. 2, pp. 589-639, Apr (1998)

[4] D. D. Nolte, Introduction to Modern Dynamics : Chaos, Networks, Space and Time, 1st ed. (Oxford University Press, 2015).

This Blog Post is a Companion to the undergraduate physics textbook Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford, 2019) introducing Lagrangians and Hamiltonians, chaos theory, complex systems, synchronization, neural networks, econophysics and Special and General Relativity.

September 22, 2021May 31, 2023 by David D. Nolte

Spontaneous Symmetry Breaking: A Mechanical Model

Symmetry is the canvas upon which the laws of physics are written. Symmetry defines the invariants of dynamical systems. But when symmetry breaks, the laws of physics break with it, sometimes in dramatic fashion. Take the Big Bang, for example, when a highly-symmetric form of the vacuum, known as the “false vacuum”, suddenly relaxed to a lower symmetry, creating an inflationary cascade of energy that burst forth as our Universe.

The early universe was extremely hot and energetic, so much so that all the forces of nature acted as one–described by a unified Lagrangian (as yet resisting discovery by theoretical physicists) of the highest symmetry. Yet as the universe expanded and cooled, the symmetry of the Lagrangian broke, and the unified forces split into two (gravity and electro-nuclear). As the universe cooled further, the Lagrangian (of the Standard Model) lost more symmetry as the electro-nuclear split into the strong nuclear force and the electro-weak force. Finally, at a tiny fraction of a second after the Big Bang, the universe cooled enough that the unified electro-week force broke into the electromagnetic force and the weak nuclear force. At each stage, spontaneous symmetry breaking occurred, and invariants of physics were broken, splitting into new behavior. In 2008, Yoichiro Nambu received the Nobel Prize in physics for his model of spontaneous symmetry breaking in subatomic physics.

Fig. 1 The spontanous symmetry breaking cascade after the Big Bang. From Ref.

Bifurcation Physics

Physics is filled with examples of spontaneous symmetry breaking. Crystallization and phase transitions are common examples. When the temperature is lowered on a fluid of molecules with high average local symmetry, the molecular interactions can suddenly impose lower-symmetry constraints on relative positions, and the liquid crystallizes into an ordered crystal. Even solid crystals can undergo a phase transition as one symmetry becomes energetically advantageous over another, and the crystal can change to a new symmetry.

In mechanics, any time a potential function evolves slowly with some parameter, it can start with one symmetry and evolve to another lower symmetry. The mechanical system governed by such a potential may undergo a discontinuous change in behavior.

In complex systems and chaos theory, sudden changes in behavior can be quite common as some parameter is changed continuously. These discontinuous changes in behavior, in response to a continuous change in a control parameter, is known as a bifurcation. There are many types of bifurcation, carrying descriptive names like the pitchfork bifurcation, period-doubling bifurcation, Hopf bifurcation, and fold bifurcation, among others. The pitchfork bifurcation is a typical example, shown in Fig. 2. As a parameter is changed continuously (horizontal axis), a stable fixed point suddenly becomes unstable and two new stable fixed points emerge at the same time. This type of bifurcation is called pitchfork because the diagram looks like a three-tined pitchfork. (This is technically called a supercritical pitchfork bifurcation. In a subcritical pitchfork bifurcation the solid and dashed lines are swapped.) This is exactly the bifurcation displayed by a simple mechanical model that illustrates spontaneous symmetry breaking.

Fig. 2 Bifurcation plot of a pitchfork bifurcation. As a parameter is changed smoothly and continuously (horizontal axis), a stable fixed point suddenly splits into three fixed points: one unstable and the other two stable.

Sliding Mass on a Rotating Hoop

One of the simplest mechanical models that displays spontaneous symmetry breaking and the pitchfork bifurcation is a bead sliding without friction on a circular hoop that is spinning on the vertical axis, as in Fig. 3. When it spins very slowly, this is just a simple pendulum with a stable equilibrium at the bottom, and it oscillates with a natural oscillation frequency ω₀ = sqrt(g/b), where b is the radius of the hoop and g is the acceleration due to gravity. On the other hand, when it spins very fast, then the bead is flung to to one side or the other by centrifugal force. The bead then oscillates around one of the two new stable fixed points, but the fixed point at the bottom of the hoop is very unstable, because any deviation to one side or the other will cause the centrifugal force to kick in. (Note that in the body frame, centrifugal force is a non-inertial force that arises in the non-inertial coordinate frame. )

Fig. 3 A bead sliding without friction on a circular hoop rotating about a vertical axis. At high speed, the bead has a stable equilibrium to either side of the vertical.

The solution uses the Euler equations for the body frame along principal axes. In order to use the standard definitions of ω₁, ω₂, and ω₃, the angle θ MUST be rotated around the x-axis. This means the x-axis points out of the page in the diagram. The y-axis is tilted up from horizontal by θ, and the z-axis is tilted from vertical by θ. This establishes the body frame.

The components of the angular velocity are

And the moments of inertia are (assuming the bead is small)

There is only one Euler equation that is non-trivial. This is for the x-axis and the angle θ. The x-axis Euler equation is

and solving for the angular acceleration gives.

This is a harmonic oscillator with a “phase transition” that occurs as ω increases from zero. At first the stable equilibrium is at the bottom. But when ω passes a critical threshold, the equilibrium angle begins to increase to a finite angle set by the rotation speed.

This can only be real if the magnitude of the argument is equal to or less than unity, which sets the critical threshold spin rate to make the system move to the new stable points to one side or the other for

which interestingly is the natural frequency of the non-rotating pendulum. Note that there are two equivalent angles (positive and negative), so this problem has a degeneracy.

This is an example of a dynamical phase transition that leads to spontaneous symmetry breaking and a pitchfork bifurcation. By integrating the angular acceleration we can get the effective potential for the problem. One contribution to the potential is due to gravity. The other is centrifugal force. When combined and plotted in Fig. 4 for a family of values of the spin rate ω, a pitchfork emerges naturally by tracing the minima in the effective potential. The values of the new equilibrium angles are given in Fig. 2.

Fig. 4 Effective potential as a function of angle for a family of spin rates. At the transition spin rate, the effective potential is essentially flat with zero natural frequency. The pitchfork is the dashed green line.

Below the transition threshold for ω, the bottom of the hoop is the equilibrium position. To find the natural frequency of oscillation, expand the acceleration expression

For small oscillations the natural frequency is given by

As the effective potential gets flatter, the natural oscillation frequency decreases until it vanishes at the transition spin frequency. As the hoop spins even faster, the new equilibrium positions emerge. To find the natural frequency of the new equilibria, expand θ around the new equilibrium θ’ = θ – θ₀

Which is a harmonic oscillator with oscillation angular frequency

Note that this is zero frequency at the transition threshold, then rises to match the spin rate of the hoop at high frequency. The natural oscillation frequency as a function of the spin looks like Fig. 5.

Fig. 5 Angular oscillation frequency for the bead. The bifurcation occurs at the critical spin rate ω = sqrt(g/b).

This mechanical analog is highly relevant for the spontaneous symmetry breaking that occurs in ferroelectric crystals when they go through a ferroelectric transition. At high temperature, these crystals have no internal polarization. But as the crystal cools towards the ferroelectric transition temperature, the optical-mode phonon modes “soften” as the phonon frequency decreases and vanishes at the transition temperature when the crystal spontaneously polarizes in one of several equivalent directions. The observation of mode softening in a polar crystal is one signature of an impending ferroelectric phase transition. Our mass on the hoop captures this qualitative physics nicely.

Golden Behavior

For fun, let’s find at what spin frequency the harmonic oscillation frequency at the dynamic equilibria equal the original natural frequency of the pendulum. Then

which is the golden ratio. It’s spooky how often the golden ratio appears in random physics problems!

This Blog Post is a Companion to the textbook Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford, 2019) that introduces topics of classical dynamics, Lagrangians and Hamiltonians, chaos theory, complex systems, synchronization, neural networks, econophysics and Special and General Relativity.

March 14, 2021June 1, 2023 by David D. Nolte

The Butterfly Effect versus the Divergence Meter: The Physics of Stein’s Gate

Imagine if you just discovered how to text through time, i.e. time-texting, when a close friend meets a shocking death. Wouldn’t you text yourself in the past to try to prevent it? But what if, every time you change the time-line and alter the future in untold ways, the friend continues to die, and you seemingly can never stop it? This is the premise of Stein’s Gate, a Japanese sci-fi animé bringing in the paradoxes of time travel, casting CERN as an evil clandestine spy agency, and introducing do-it-yourself inventors, hackers, and wacky characters, while it centers on a terrible death of a lovable character that can never be avoided.

It is also a good computational physics project that explores the dynamics of bifurcations, bistability and chaos. I teach a course in modern dynamics in the Physics Department at Purdue University. The topics of the course range broadly from classical mechanics to chaos theory, social networks, synchronization, nonlinear dynamics, economic dynamics, population dynamics, evolutionary dynamics, neural networks, special and general relativity, among others that are covered in the course using a textbook that takes a modern view of dynamics [1].

For the final project of the second semester the students (Junior physics majors) are asked to combine two or three of the topics into a single project. Students have come up with a lot of creative combinations: population dynamics of zombies, nonlinear dynamics of negative gravitational mass, percolation of misinformation in presidential elections, evolutionary dynamics of neural architecture, and many more. In that spirit, and for a little fun, in this blog I explore the so-called physics of Stein’s Gate.

Stein’s Gate and the Divergence Meter

Stein’s Gate is a Japanese TV animé series that had a world-wide distribution in 2011. The central premise of the plot is that certain events always occur even if you are on different timelines—like trying to avoid someone’s death in an accident.

This is the problem confronting Rintaro Okabe who tries to stop an accident that kills his friend Mayuri Shiina. But every time he tries to change time, she dies in some other way. It turns out that all the nearby timelines involve her death. According to a device known as The Divergence Meter, Rintaro must get farther than 4% away from the original timeline to have a chance to avoid the otherwise unavoidable event.

This is new. Usually, time-travel Sci-Fi is based on the Butterfly Effect. Chaos theory is characterized by something called sensitivity to initial conditions (SIC), meaning that slightly different starting points produce trajectories that diverge exponentially from nearby trajectories. It is called the Butterfly Effect because of the whimsical notion that a butterfly flapping its wings in China can cause a hurricane in Florida. In the context of the butterfly effect, if you go back in time and change anything at all, the effect cascades through time until the present time in unrecognizable. As an example, in one episode of the TV cartoon The Simpsons, Homer goes back in time to the age of the dinosaurs and kills a single mosquito. When he gets back to our time, everything has changed in bazaar and funny ways.

Stein’s Gate introduces a creative counter example to the Butterfly Effect. Instead of scrambling the future when you fiddle with the past, you find that you always get the same event, even when you change a lot of the conditions—Mayuri still dies. This sounds eerily familiar to a physicist who knows something about chaos theory. It means that the unavoidable event is acting like a stable fixed point in the time dynamics—an attractor! Even if you change the initial conditions, the dynamics draw you back to the fixed point—in this case Mayuri’s accident. What would this look like in a dynamical system?

The Local Basin of Attraction

Dynamical systems can be described as trajectories in a high-dimensional state space. Within state space there are special points where the dynamics are static—known as fixed points. For a stable fixed point, a slight perturbation away will relax back to the fixed point. For an unstable fixed point, on the other hand, a slight perturbation grows and the system dynamics evolve away. However, there can be regions in state space where every initial condition leads to trajectories that stay within that region. This is known as a basin of attraction, and the boundaries of these basins are called separatrixes.

A high-dimensional state space can have many basins of attraction. All the physics that starts within a basin stays within that basin—almost like its own self-consistent universe, bordered by countless other universes. There are well-known physical systems that have many basins of attraction. String theory is suspected to generate many adjacent universes where the physical laws are a little different in each basin of attraction. Spin glasses, which are amorphous solid-state magnets, have this property, as do recurrent neural networks like the Hopfield network. Basins of attraction occur naturally within the physics of these systems.

It is possible to embed basins of attraction within an existing dynamical system. As an example, let’s start with one of the simplest types of dynamics, a hyperbolic fixed point

that has a single saddle fixed point at the origin. We want to add a basin of attraction at the origin with a domain range given by a radius r₀. At the same time, we want to create a separatrix that keeps the outer hyperbolic dynamics separate from the internal basin dynamics. To keep all outer trajectories in the outer domain, we can build a dynamical barrier to prevent the trajectories from crossing the separatrix. This can be accomplished by adding a radial repulsive term

In x-y coordinates this is

We also want to keep the internal dynamics of our basin separate from the external dynamics. To do this, we can multiply by a sigmoid function, like a Heaviside function H(r-r₀), to zero-out the external dynamics inside our basin. The final external dynamics is then

Now we have to add the internal dynamics for the basin of attraction. To make it a little more interesting, let’s make the internal dynamics an autonomous oscillator

Putting this all together, gives

This looks a little complex, for such a simple model, but it illustrates the principle. The sigmoid is best if it is differentiable, so instead of a Heaviside function it can be a Fermi function

The phase-space portrait of the final dynamics looks like

Figure 1. Hyperbolic dynamics with a basin of attraction embedded inside it at the origin. The dynamics inside the basin of attraction is a limit cycle.

Adding the internal dynamics does not change the far-field external dynamics, which are still hyperbolic. The repulsive term does split the central saddle point into two saddle points, one on each side left-and-right, so the repulsive term actually splits the dynamics. But the internal dynamics are self-contained and separate from the external dynamics. The origin is an unstable spiral that evolves to a limit cycle. The basin boundary has marginal stability and is known as a “wall”.

To verify the stability of the external fixed point, find the fixed point coordinates

and evaluate the Jacobian matrix (for A = 1 and x₀ = 2)

which is clearly a saddle point because the determinant is negative.

In the context of Stein’s Gate, the basin boundary is equivalent to the 4% divergence which is necessary to escape the internal basin of attraction where Mayuri meets her fate.

Python Program: SteinsGate2D.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
SteinsGate2D.py
Created on Sat March 6, 2021

@author: David Nolte
Introduction to Modern Dynamics, 2nd edition (Oxford University Press, 2019)

2D simulation of Stein's Gate Divergence Meter
"""
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt

plt.close('all')

def solve_flow(param,lim = [-6,6,-6,6],max_time=20.0):

    def flow_deriv(x_y, t0, alpha, beta, gamma):
        #"""Compute the time-derivative ."""
        x, y = x_y
        
        w = 1
        R2 = x**2 + y**2
        R = np.sqrt(R2)
        arg = (R-2)/0.1
        env1 = 1/(1+np.exp(arg))
        env2 = 1 - env1
        
        f = env2*(x*(1/(R-1.99)**2 + 1e-2) - x) + env1*(w*y + w*x*(1 - R))
        g = env2*(y*(1/(R-1.99)**2 + 1e-2) + y) + env1*(-w*x + w*y*(1 - R))
        
        return [f,g]
    model_title = 'Steins Gate'

    plt.figure()
    xmin = lim[0]
    xmax = lim[1]
    ymin = lim[2]
    ymax = lim[3]
    plt.axis([xmin, xmax, ymin, ymax])

    N = 24*4 + 47
    x0 = np.zeros(shape=(N,2))
    ind = -1
    for i in range(0,24):
        ind = ind + 1
        x0[ind,0] = xmin + (xmax-xmin)*i/23
        x0[ind,1] = ymin
        ind = ind + 1
        x0[ind,0] = xmin + (xmax-xmin)*i/23
        x0[ind,1] = ymax
        ind = ind + 1
        x0[ind,0] = xmin
        x0[ind,1] = ymin + (ymax-ymin)*i/23
        ind = ind + 1
        x0[ind,0] = xmax
        x0[ind,1] = ymin + (ymax-ymin)*i/23
    ind = ind + 1
    x0[ind,0] = 0.05
    x0[ind,1] = 0.05
    
    for thetloop in range(0,10):
        ind = ind + 1
        theta = 2*np.pi*(thetloop)/10
        ys = 0.125*np.sin(theta)
        xs = 0.125*np.cos(theta)
        x0[ind,0] = xs
        x0[ind,1] = ys

    for thetloop in range(0,10):
        ind = ind + 1
        theta = 2*np.pi*(thetloop)/10
        ys = 1.7*np.sin(theta)
        xs = 1.7*np.cos(theta)
        x0[ind,0] = xs
        x0[ind,1] = ys

    for thetloop in range(0,20):
        ind = ind + 1
        theta = 2*np.pi*(thetloop)/20
        ys = 2*np.sin(theta)
        xs = 2*np.cos(theta)
        x0[ind,0] = xs
        x0[ind,1] = ys
        
    ind = ind + 1
    x0[ind,0] = -3
    x0[ind,1] = 0.05
    ind = ind + 1
    x0[ind,0] = -3
    x0[ind,1] = -0.05
    ind = ind + 1
    x0[ind,0] = 3
    x0[ind,1] = 0.05
    ind = ind + 1
    x0[ind,0] = 3
    x0[ind,1] = -0.05
    ind = ind + 1
    x0[ind,0] = -6
    x0[ind,1] = 0.00
    ind = ind + 1
    x0[ind,0] = 6
    x0[ind,1] = 0.00
           
    colors = plt.cm.prism(np.linspace(0, 1, N))
                        
    # Solve for the trajectories
    t = np.linspace(0, max_time, int(250*max_time))
    x_t = np.asarray([integrate.odeint(flow_deriv, x0i, t, param)
                      for x0i in x0])

    for i in range(N):
        x, y = x_t[i,:,:].T
        lines = plt.plot(x, y, '-', c=colors[i])
        plt.setp(lines, linewidth=1)
       
    plt.show()
    plt.title(model_title)
        
    return t, x_t

param = (0.02,0.5,0.2)        # Steins Gate
lim = (-6,6,-6,6)

t, x_t = solve_flow(param,lim)

plt.savefig('Steins Gate')

The Lorenz Butterfly

Two-dimensional phase space cannot support chaos, and we would like to reconnect the central theme of Stein’s Gate, the Divergence Meter, with the Butterfly Effect. Therefore, let’s actually incorporate our basin of attraction inside the classic Lorenz Butterfly. The goal is to put an attracting domain into the midst of the three-dimensional state space of the Lorenz butterfly in a way that repels the butterfly, without destroying it, but attracts local trajectories. The question is whether the butterfly can survive if part of its state space is made unavailable to it.

The classic Lorenz dynamical system is

As in the 2D case, we will put in a repelling barrier that prevents external trajectories from moving into the local basin, and we will isolate the external dynamics by using the sigmoid function. The final flow equations looks like

where the radius is relative to the center of the attracting basin

and r₀ is the radius of the basin. The center of the basin is at [x₀, y₀, z₀] and we are assuming that x₀ = 0 and y₀ = 0 and z₀ = 25 for the standard Butterfly parameters p = 10, r = 25 and b = 8/3. This puts our basin of attraction a little on the high side of the center of the Butterfly. If we embed it too far inside the Butterfly it does actually destroy the Butterfly dynamics.

When r₀ = 0, the dynamics of the Lorenz’ Butterfly are essentially unchanged. However, when r₀ = 1.5, then there is a repulsive effect on trajectories that pass close to the basin. It can be seen as part of the trajectory skips around the outside of the basin in Figure 2.

Figure 2. The Lorenz Butterfly with part of the trajectory avoiding the basin that is located a bit above the center of the Butterfly.

Trajectories can begin very close to the basin, but still on the outside of the separatrix, as in the top row of Figure 3 where the basin of attraction with r₀ = 1.5 lies a bit above the center of the Butterfly. The Butterfly still exists for the external dynamics. However, any trajectory that starts within the basin of attraction remains there and executes a stable limit cycle. This is the world where Mayuri dies inside the 4% divergence. But if the initial condition can exceed 4%, then the Butterfly effect takes over. The bottom row of Figure 2 shows that the Butterfly itself is fragile. When the external dynamics are perturbed more strongly by more closely centering the local basin, the hyperbolic dynamics of the Butterfly are impeded and the external dynamics are converted to a stable limit cycle. It is interesting that the Butterfly, so often used as an illustration of sensitivity to initial conditions (SIC), is itself sensitive to perturbations that can convert it away from chaos and back to regular motion.

Figure 3. (Top row) A basin of attraction is embedded a little above the Butterfly. The Butterfly still exists for external trajectories, but any trajectory that starts inside the basin of attraction remains inside the basin. (Bottom row) The basin of attraction is closer to the center of the Butterfly and disrupts the hyperbolic point and converts the Butterfly into a stable limit cycle.

Discussion and Extensions

In the examples shown here, the local basin of attraction was put in “by hand” as an isolated region inside the dynamics. It would be interesting to consider more natural systems, like a spin glass or a Hopfield network, where the basins of attraction occur naturally from the physical principles of the system. Then we could use the “Divergence Meter” to explore these physical systems to see how far the dynamics can diverge before crossing a separatrix. These systems are impossible to visualize because they are intrinsically very high dimensional systems, but Monte Carlo approaches could be used to probe the “sizes” of the basins.

Another interesting extension would be to embed these complex dynamics into spacetime. Since this all started with the idea of texting through time, it would be interesting (and challenging) to see how we could describe this process in a high dimensional Minkowski space that had many space dimensions (but still only one time dimension). Certainly it would violate the speed of light criterion, but we could then take the approach of David Deutsch and view the time axis as if it had multiple branches, like the branches of the arctangent function, creating time-consistent sheets within a sheave of flat Minkowski spaces.

References

[1] D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd edition (Oxford University Press, 2019)

[2] E. N. Lorenz, The essence of chaos. (University of Washington Press, 1993)

[3] E. N. Lorenz, “Deterministic Nonperiodic Flow,” Journal of the Atmospheric Sciences, vol. 20, no. 2, pp. 130-141, 1963 (1963)

November 16, 2020October 24, 2023 by David D. Nolte

Edward Lorenz’ Chaotic Butterfly

The butterfly effect is one of the most widely known principles of chaos theory. It has become a meme, propagating through popular culture in movies, books, TV shows and even casual conversation.

Can a butterfly flapping its wings in Florida send a hurricane to New York?

The origin of the butterfly effect is — not surprisingly — the image of a butterfly-like set of trajectories that was generated, in one of the first computer simulations of chaos theory, by Edward Lorenz.

Lorenz’ Butterfly

Excerpted from Galileo Unbound (Oxford, 2018) pg. 215

When Edward Lorenz (1917 – 2008) was a child, he memorized all perfect squares up to ten thousand. This obvious interest in mathematics led him to a master’s degree in the subject at Harvard in 1940 under the supervision of Georg Birkhoff. Lorenz’s master’s thesis was on an aspect of Riemannian geometry, but his foray into nonlinear dynamics was triggered by the intervention of World War II. Only a few months before receiving his doctorate in mathematics from Harvard, the Japanese bombed Pearl Harbor.

Lorenz left the PhD program at Harvard to join the United States Army Air Force to train as a weather forecaster in early 1942, and he took courses on forecasting and meteorology at MIT. After receiving a second master’s degree, this time in meteorology, Lorenz was posted to Hawaii, then to Saipan and finally to Guam. His area of expertise was in high-level winds, which were important for high-altitude bombing missions during the final months of the war in the Pacific. After the Japanese surrender, Lorenz returned to MIT, where he continued his studies in meteorology, receiving his doctorate degree in 1948 with a thesis on the application of fluid dynamical equations to predict the motion of storms.

One of Lorenz’ colleagues at MIT was Norbert Wiener (1894 – 1964), with whom he sometimes played chess during lunch at the faculty club. Wiener had published his landmark book Cybernetics: Control and Communication in the Animal and Machine in 1949 which arose out of the apparently mundane problem of gunnery control during the Second World War. As an abstract mathematician, Wiener attempted to apply his cybernetic theory to the complexities of weather, but he developed a theorem concerning nonlinear fluid dynamics which appeared to show that linear interpolation, of sufficient resolution, would suffice for weather forecasting, possibly even long-range forecasting. Many on the meteorology faculty embraced this theorem because it fell in line with common practices of the day in which tomorrow’s weather was predicted using linear regression on measurements taken today. However, Lorenz was skeptical, having acquired a detailed understanding of atmospheric energy cascades as larger vortices induced smaller vortices all the way down to the molecular level, dissipating as heat, and then all the way back up again as heat drove large-scale convection. This was clearly not a system that would yield to linearization. Therefore, Lorenz determined to solve nonlinear fluid dynamics models to test this conjecture.

Even with a computer in hand, the atmospheric equations needed to be simplified to make the calculations tractable. Lorenz was more a scientist than an engineer, and more of a meteorologist than a forecaster. He did not hesitate to make simplifying assumptions if they retained the correct phenomenological behavior, even if they no longer allowed for accurate weather predictions.

He had simplified the number of atmospheric equations down to twelve. Progress was good, and by 1961, he had completed a large initial numerical study. He focused on nonperiodic solutions, which he suspected would deviate significantly from the predictions made by linear regression, and this hunch was vindicated by his numerical output. One day, as he was testing his results, he decided to save time by starting the computations midway by using mid-point results from a previous run as initial conditions. He typed in the three-digit numbers from a paper printout and went down the hall for a cup of coffee. When he returned, he looked at the printout of the twelve variables and was disappointed to find that they were not related to the previous full-time run. He immediately suspected a faulty vacuum tube, as often happened. But as he looked closer at the numbers, he realized that, at first, they tracked very well with the original run, but then began to diverge more and more rapidly until they lost all connection with the first-run numbers. His initial conditions were correct to a part in a thousand, but this small error was magnified exponentially as the solution progressed.

The Lorenz Equations

Over the following months, he was able to show that SIC was a result of the nonperiodic solutions. The more Lorenz became familiar with the behavior of his equations, the more he felt that the 12-dimensional trajectories had a repeatable shape. He tried to visualize this shape, to get a sense of its character, but it is difficult to visualize things in twelve dimensions, and progress was slow. Then Lorenz found that when the solution was nonperiodic (the necessary condition for SIC), four of the variables settled down to zero, leaving all the dynamics to the remaining three variables.

Lorenz narrowed the equations of atmospheric instability down to three variables: the stream function, the change in temperature and the deviation in linear temperature. The only parameter in the stream function is something known as the Prandtl Number. This is a dimensionless number which is the ratio of the kinetic viscosity of the fluid to its thermal diffusion coefficient and is a physical property of the fluid. The only parameter in the change in temperature is the Rayleigh Number which is a dimensionless parameter proportional to the difference in temperature between the top and the bottom of the fluid layer. The final parameter, in the equation for the deviation in linear temperature, is the ratio of the height of the fluid layer to the width of the convection rolls. The final simplified model is given by the flow equations

The Butterfly

Lorenz finally had a 3-variable dynamical system that displayed chaos. Moreover, it had a three-dimensional state space that could be visualized directly. He ran his simulations, exploring the shape of the trajectories in three-dimensional state space for a wide range of initial conditions, and the trajectories did indeed always settle down to restricted regions of state space. They relaxed in all cases to a sort of surface that was elegantly warped, with wing-like patterns like a butterfly, as the state point of the system followed its dynamics through time. The attractor of the Lorenz equations was strange. Later, in 1971, David Ruelle (1935 – ), a Belgian-French mathematical physicist named this a “strange attractor”, and this name has become a standard part of the language of the theory of chaos.

The first graphical representation of the butterfly attractor is shown in Fig. 1 drawn by Lorenz for his 1963 publication.

Fig. 1 *Excerpts of the title, abstract and sections of Lorenz’ 1963 paper. His three-dimensional flow equations produce trajectories that relax onto a three-dimensional “strange attractor*“.

Using our modern plotting ability, the 3D character of the butterfly is shown in Fig. 2

*Fig. 2 Edward Lorenz’ chaotic butterfly*

A projection onto the x-y plane is shown in Fig. 3. In the full 3D state space the trajectories never overlap, but in the projection onto a 2D plane the trajectories are moving above and below each other.

Fig. 3 Projection of the butterfly onto the x-y plane centered on the origin.

The reason it is called a strange attractor is because all initial conditions relax onto the strange attractor, yet every trajectory on the strange attractor separates exponentially from neighboring trajectories, displaying the classic SIC property of chaos. So here is an elegant collection of trajectories that are certainly not just random noise, yet detailed prediction is still impossible. Deterministic chaos has significant structure, and generates beautiful patterns, without actual “randomness”.

Python Program

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 16 07:38:57 2018

@author: nolte
Introduction to Modern Dynamics, 2nd edition (Oxford University Press, 2019)

Lorenz model of atmospheric turbulence
"""
import numpy as np
import matplotlib as mpl

import matplotlib.colors as colors
import matplotlib.cm as cmx

from scipy import integrate
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import cnames
from matplotlib import animation

plt.close('all')

jet = cm = plt.get_cmap('jet') 
values = range(10)
cNorm  = colors.Normalize(vmin=0, vmax=values[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)

def solve_lorenz(N=12, angle=0.0, max_time=8.0, sigma=10.0, beta=8./3, rho=28.0):

    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1], projection='3d')
    ax.axis('off')

    # prepare the axes limits
    ax.set_xlim((-25, 25))
    ax.set_ylim((-35, 35))
    ax.set_zlim((5, 55))

    def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):
        """Compute the time-derivative of a Lorenz system."""
        x, y, z = x_y_z
        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]

    # Choose random starting points, uniformly distributed from -15 to 15
    np.random.seed(1)
    x0 = -10 + 20 * np.random.random((N, 3))

    # Solve for the trajectories
    t = np.linspace(0, max_time, int(500*max_time))
    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)
                      for x0i in x0])

    # choose a different color for each trajectory
    # colors = plt.cm.viridis(np.linspace(0, 1, N))
    # colors = plt.cm.rainbow(np.linspace(0, 1, N))
    # colors = plt.cm.spectral(np.linspace(0, 1, N))
    colors = plt.cm.prism(np.linspace(0, 1, N))

    for i in range(N):
        x, y, z = x_t[i,:,:].T
        lines = ax.plot(x, y, z, '-', c=colors[i])
        plt.setp(lines, linewidth=1)

    ax.view_init(30, angle)
    plt.show()

    return t, x_t


t, x_t = solve_lorenz(angle=0, N=12)

plt.figure(2)
lines = plt.plot(t,x_t[1,:,0],t,x_t[1,:,1],t,x_t[1,:,2])
plt.setp(lines, linewidth=1)
lines = plt.plot(t,x_t[2,:,0],t,x_t[2,:,1],t,x_t[2,:,2])
plt.setp(lines, linewidth=1)
lines = plt.plot(t,x_t[10,:,0],t,x_t[10,:,1],t,x_t[10,:,2])
plt.setp(lines, linewidth=1)

To explore the parameter space of the Lorenz attractor, the key parameters to change are sigma (the Prandtl number), r (the Rayleigh number) and b on line 31 of the Python code.

References

[1] E. N. Lorenz, The essence of chaos (The Jessie and John Danz lectures; Jessie and John Danz lectures.). Seattle :: University of Washington Press (in English), 1993.

[2] E. N. Lorenz, “Deterministic Nonperiodic Flow,” Journal of the Atmospheric Sciences, vol. 20, no. 2, pp. 130-141, 1963 (1963)

The Physics of Life, the Universe and Everything:

Henri Poincaré and his Homoclinic Tangle

Will the next extinction-scale asteroid strike the Earth in our lifetime?

This existential question—the question of our continued existence on this planet—is rhetorical, because there are far too many bodies in our solar system to accurately calculate all trajectories of all asteroids.

The solar system is what is known as an N-body problem. And even the N is not well determined. The asteroid belt alone has over a million extinction-sized asteroids, and there are tens of millions of smaller ones that could still do major damage to life on Earth if they hit. To have a hope of calculating even one asteroid trajectory do we ignore planetary masses that are too small? What is too small? What if we only consider the Sun, the Earth and Jupiter? This is what Euler did in 1760, and he still had to make more assumptions.

Stability of the Solar System

Once Newton published his Principia, there was a pressing need to calculate the orbit of the Moon (see my blog post on the three-body problem). This was important for navigation, because if the daily position of the moon could be known with sufficient accuracy, then ships would have a means to determine their longitude at sea. However, the Moon, Earth and Sun are already a three-body problem, which still ignores the effects of Mars and Jupiter on the Moon’s orbit, not to mention the problem that the Earth is not a perfect sphere. Therefore, to have any hope of success, toy systems that were stripped of all their obfuscating detail were needed.

Euler investigated simplified versions of the three-body problem around 1760, treating a body attracted to two fixed centers of gravity moving in the plane, and he solved it using elliptic integrals. When the two fixed centers are viewed in a coordinate frame that is rotating with the Sun-Earth system, it can come close to capturing many of the important details of the system. In 1762 Euler tried another approach, called the restricted three-body problem, where he considered a massless Moon attracted to a massive Earth orbiting a massive Sun, again all in the plane. Euler could not find general solutions to this problem, but he did stumble on an interesting special case when the three bodies remain collinear throughout their motions in a rotating reference frame.

It was not the danger of asteroids that was the main topic of interest in those days, but the question whether the Earth itself is in a stable orbit and is safe from being ejected from the Solar system. Despite steadily improving methods for calculating astronomical trajectories through the nineteenth century, this question of stability remained open.

Poincaré and the King Oscar Prize of 1889

Some years ago I wrote an article for Physics Today called “The Tangled Tale of Phase Space” that tracks the historical development of phase space. One of the chief players in that story was Henri Poincaré (1854 – 1912). Henri Poincare was the Einstein before Einstein. He was a minor celebrity and was considered to be the greatest genius of his era. The event in his early career that helped launch him to stardom was a mathematics prize announced in 1887 to honor the birthday of King Oscar II of Sweden. The challenge problem was as simple as it was profound: Prove rigorously whether the solar system is stable.

This was the old N-body problem that had so far resisted solution, but there was a sense at that time that recent mathematical advances might make the proof possible. There was even a rumor that Dirichlet had outlined such a proof, but no trace of the outline could be found in his papers after his death in 1859.

The prize competition was announced in Acta Mathematica, written by the Swedish mathematician Gösta Mittag-Leffler. It stated:

Given a system of arbitrarily many mass points that attract each according to Newton’s law, under the assumption that no two points ever collide, try to find a representation of the coordinates of each point as a series in a variable that is some known function of time and for all of whose values the series converges uniformly.

The timing of the prize was perfect for Poincaré who was in his early thirties and just beginning to make his mark on mathematics. He was working on the theory of dynamical systems and was developing a new viewpoint that went beyond integrating single trajectories by focusing more broadly on whole classes of solutions. The question of the stability of the solar system seemed like a good problem to use to sharpen his mathematical tools. The general problem was still too difficult, so he began with Euler’s restricted three-body problem. He made steady progress, and along the way he invented an array of new techniques for studying the general properties of dynamical systems. One of these was the Poincaré section. Another was his set of integral invariants, one of which is recognized as the conservation of volume in phase space, also known as Liouville’s theorem, although it was Ludwig Boltzmann who first derived this result (see my Physics Today article). Eventually, he believed he had proven that the restricted three-body problem was stable.

By the time Poincaré had finished is prize submission, he had invented a new field of mathematical analysis, and the judges of the prize submission recognized it. Poincaré was named the winner, and his submission was prepared for publication in the Acta. However, Mittag-Leffler was a little concerned by a technical objection that had been raised, so he forwarded the comment to Poincaré for him to look at. At first, Poincaré thought the objection could easily be overcome, but as he worked on it and delved deeper, he had a sudden attack of panic. Trajectories near a saddle point did not converge. His proof of stability was wrong!

He alerted Mittag-Leffler to stop the presses, but it was too late. The first printing had been completed and review copies had already been sent to the judges. Mittag-Leffler immediately wrote to them asking for their return while Poincaré worked nonstop to produce a corrected copy. When he had completed his reanalysis, he had discovered a divergent feature of the solution to the dynamical problem near saddle points that is recognized today as the discovery of chaos. Poincaré paid for the reprinting of his paper out of his own pocket and (almost) all of the original printing was destroyed. This embarrassing moment in the life of a great mathematician was virtually forgotten until it was brought to light by the historian Barrow-Green in 1994 [1].

Poincaré is still a popular icon in France. Here is the Poincaré cafe in Paris.

A crater on the Moon is named after Poincaré.

Chaos in the Poincaré Return Map

Despite the fact that his conclusions on the stability of the 3-body problem flipped, Poincaré’s new tools for analyzing dynamical systems earned him the prize. He did not stop at his modified prize submission but continued working on systematizing his methods, publishing New Methods in Celestial Mechanics in several volumes through the 1890’s. It was here that he fully explored what happens when a trajectory approaches a saddle point of dynamical equilibrium.

The third volume of a three-book series that grew from Poincaré’s award-winning paper

To visualize a periodic trajectory, Poincaré invented a mathematical tool called a “first-return map”, also known as a Poincaré section. It was a way of taking a higher dimensional continuous trajectory and turning it into a simple iterated discrete map. Therefore, one did not need to solve continuous differential equations, it was enough to just iterate the map. In this way, complicated periodic, or nearly periodic, behavior could be explored numerically. However, even armed with this weapon, Poincaré found that iterated maps became unstable as a trajectory that originated from a saddle point approached another equivalent saddle point. Because the dynamics are periodic, the outgoing and incoming trajectories are opposite ends of the same trajectory, repeated with 2-pi periodicity. Therefore, the saddle point is also called a homoclinic point, meaning that trajectories in the discrete map intersect with themselves. (If two different trajectories in the map intersect, that is called a heteroclinic point.) When Poincaré calculated the iterations around the homoclinic point, he discovered a wild and complicated pattern in which a trajectory intersected itself many times. Poincaré wrote:

[I]f one seeks to visualize the pattern formed by these two curves and their infinite number of intersections … these intersections form a kind of lattice work, a weave, a chain-link network of infinitely fine mesh; each of the two curves can never cross itself, but it must fold back on itself in a very complicated way so as to recross all the chain-links an infinite number of times .… One will be struck by the complexity of this figure, which I am not even attempting to draw. Nothing can give us a better idea of the intricacy of the three-body problem, and of all the problems of dynamics in general…

This was the discovery of chaos! Today we call this “lattice work” the “homoclinic tangle”. He could not draw it with the tools of his day … but we can!

Chirikov’s Standard Map

The restricted 3-body problem is a bit more complicated than is needed to illustrate Poincaré’s homoclinic tangle. A much simpler model is a discrete map called Chirikov’s Map or the Standard Map. It describes the Poincaré section of a periodically kicked oscillator that rotates or oscillates in the angular direction with an angular momentm J. The map has the simple form

in which the angular momentum in updated first, and then the angle variable is updated with the new angular momentum. When plotted on the (θ,J) plane, the standard map produces a beautiful kaleidograph of intertwined trajectories piercing the Poincaré plane, as shown in the figure below. The small points or dots are successive intersections of the higher-dimensional trajectory intersecting a plane. It is possible to trace successive points by starting very close to a saddle point (on the left) and connecting successive iterates with lines. These lines merge into the black trace in the figure that emerges along the unstable manifold of the saddle point on the left and approaches the saddle point on the right generally along the stable manifold.

Fig. Standard map for K = 0.97 at the transition to full chaos. The dark line is the trajectory of the unstable manifold emerging from the saddle point at (p,0). Note the wild oscillations as it approaches the saddle point at (3pi,0).

However, as the successive iterates approach the new saddle (which is really just the old saddle point because of periodicity) it crosses the stable manifold again and again, in ever wilder swings that diverge as it approaches the saddle point. This is just one trace. By calculating traces along all four stable and unstable manifolds and carrying them through to the saddle, a lattice work, or homoclinic tangle emerges.

Two of those traces originate from the stable manifolds, so to calculate their contributions to the homoclinic tangle, one must run these traces backwards in time using the inverse Chirikov map. This is

The four traces all intertwine at the saddle point in the figure below with a zoom in on the tangle in the next figure. This is the lattice work that Poincaré glimpsed in 1889 as he worked feverishly to correct the manuscript that won him the prize that established him as one of the preeminent mathematicians of Europe.

Fig. The homoclinic tangle caused by the folding of phase space trajectories as stable and unstable manifolds criss-cross in the Poincare map at the saddle point. This was the figure that Poincaré could not attempt to draw because of its complexity.

Fig. A zoom-in of the homoclinic tangle at the saddle point as the stable and unstable manifolds create a lattice of intersections. This is the fundamental origin of chaos and the sensitivity to initial conditions (SIC) that make forecasting almost impossible in chaotic systems.

The Physics of Life, the Universe and Everything:

Python Code: StandmapHom.py

(Python code on GitHub.)

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
StandmapHom.py
Created on Sun Aug  2  2020
"Introduction to Modern Dynamics" 2nd Edition (Oxford, 2019)
@author: nolte
"""

import numpy as np
from matplotlib import pyplot as plt
from numpy import linalg as LA

plt.close('all')

eps = 0.97

np.random.seed(2)

plt.figure(1)

for eloop in range(0,100):

    rlast = 2*np.pi*(0.5-np.random.random())
    thlast = 4*np.pi*np.random.random()
    
    rplot = np.zeros(shape=(200,))
    thetaplot = np.zeros(shape=(200,))
    for loop in range(0,200):
        rnew = rlast + eps*np.sin(thlast)
        thnew = np.mod(thlast+rnew,4*np.pi)
        
        thetaplot[loop] = np.mod(thnew-np.pi,4*np.pi)     
        rtemp = np.mod(rnew + np.pi,2*np.pi)
        rplot[loop] = rtemp - np.pi
  
        rlast = rnew
        thlast = thnew
        
    plt.plot(np.real(thetaplot),np.real(rplot),'o',ms=0.2)
    plt.xlim(xmin=np.pi,xmax=4*np.pi)
    plt.ylim(ymin=-2.5,ymax=2.5)
        
plt.savefig('StandMap')

K = eps
eps0 = 5e-7

J = [[1,1+K],[1,1]]
w, v = LA.eig(J)

My = w[0]
Vu = v[:,0]     # unstable manifold
Vs = v[:,1]     # stable manifold

# Plot the unstable manifold
Hr = np.zeros(shape=(100,150))
Ht = np.zeros(shape=(100,150))
for eloop in range(0,100):
    
    eps = eps0*eloop

    roldu1 = eps*Vu[0]
    thetoldu1 = eps*Vu[1]
    
    Nloop = np.ceil(-6*np.log(eps0)/np.log(eloop+2))
    flag = 1
    cnt = 0
    
    while flag==1 and cnt < Nloop:
        
        ru1 = roldu1 + K*np.sin(thetoldu1)
        thetau1 = thetoldu1 + ru1
        
        roldu1 = ru1
        thetoldu1 = thetau1
        
        if thetau1 > 4*np.pi:
            flag = 0
            
        Hr[eloop,cnt] = roldu1
        Ht[eloop,cnt] = thetoldu1 + 3*np.pi
        cnt = cnt+1
    
x = Ht[0:99,12] - 2*np.pi
x2 = 6*np.pi - x
y = Hr[0:99,12]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[5:39,15] - 2*np.pi
x2 = 6*np.pi - x
y = Hr[5:39,15]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[12:69,16] - 2*np.pi
x2 = 6*np.pi - x
y = Hr[12:69,16]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[15:89,17] - 2*np.pi
x2 = 6*np.pi - x
y = Hr[15:89,17]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[30:99,18] - 2*np.pi
x2 = 6*np.pi - x
y = Hr[30:99,18]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

# Plot the stable manifold
del Hr, Ht
Hr = np.zeros(shape=(100,150))
Ht = np.zeros(shape=(100,150))
#eps0 = 0.03
for eloop in range(0,100):
    
    eps = eps0*eloop

    roldu1 = eps*Vs[0]
    thetoldu1 = eps*Vs[1]
    
    Nloop = np.ceil(-6*np.log(eps0)/np.log(eloop+2))
    flag = 1
    cnt = 0
    
    while flag==1 and cnt < Nloop:
        
        thetau1 = thetoldu1 - roldu1
        ru1 = roldu1 - K*np.sin(thetau1)

        roldu1 = ru1
        thetoldu1 = thetau1
        
        if thetau1 > 4*np.pi:
            flag = 0
            
        Hr[eloop,cnt] = roldu1
        Ht[eloop,cnt] = thetoldu1
        cnt = cnt+1
    
x = Ht[0:79,12] + np.pi
x2 = 6*np.pi - x
y = Hr[0:79,12]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[4:39,15] + np.pi
x2 = 6*np.pi - x
y = Hr[4:39,15]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[12:69,16] + np.pi
x2 =  6*np.pi - x
y = Hr[12:69,16]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[15:89,17] + np.pi
x2 =  6*np.pi - x
y = Hr[15:89,17]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

del x,y
x = Ht[30:99,18] + np.pi
x2 =  6*np.pi - x
y = Hr[30:99,18]
y2 = -y
plt.plot(x,y,linewidth =0.75)
plt.plot(x2,y2,linewidth =0.75)

References

[1] D. D. Nolte, “The tangled tale of phase space,” Physics Today, vol. 63, no. 4, pp. 33-38, Apr (2010)

[2] M. C. Gutzwiller, “Moon-Earth-Sun: The oldest three-body problem,” Reviews of Modern Physics, vol. 70, no. 2, pp. 589-639, Apr (1998)

[3] Barrow-Green J. Oscar II’s Prize Competition and the Error in Poindare’s Memoir on the Three Body Problem. Arch Hist Exact Sci 48: 107-131, 1994.

[4] Barrow-Green J. Poincaré and the three body problem. London Mathematical Society, 1997.

[5] https://the-moon.us/wiki/Poincar%C3%A9

[6] Poincaré H and Goroff DL. New methods of celestial mechanics … Edited and introduced by Daniel L. Goroff. New York: American Institute of Physics, 1993.

Henri Poincare (1889)

Cartwight and Littlewood (1945)

Andrey Kolmogorov (1954)

Edward Lorenz (1963)

V. I. Arnold (1964)

Sephen Smale (1967)

Ruelle and Takens (1971)

Feigenbaum (1978)

Gleick (1987)

References

The Zero-Measure Cantor Set

Fat Fractals

Arnold Tongues

The Rings of Saturn

References:

Phase Space

Quantum Phase Space: The Wigner Distribution Function

The Harmonic Oscillator

Quantum Pendulum and Separatrix Chaos

Quantum Double-Well and Separatrix Chaos

Conclusion

YouTube Video

For more on the history of quantum trajectories, see Galileo Unbound from Oxford Press:

References

Disaster in the Poconos

Coherent States

Quantum Trajectories

Youtube Video

YouTube Video

For more on the history of quantum trajectories, see Galileo Unbound from Oxford Press:

References

The Quantum Mechanics of Classically Chaotic Systems

The Quantum Circus

Quantum Chaos versus Laser Speckle

YouTube Video

Read more about the history of chaos theory in Galileo Unbound from Oxford Press:

References

The Three-Body Problem

Resonance

Matlab Code: body3.m

References:

Bifurcation Physics

Sliding Mass on a Rotating Hoop

Golden Behavior

Stein’s Gate and the Divergence Meter

The Local Basin of Attraction

Python Program: SteinsGate2D.py

The Lorenz Butterfly

Discussion and Extensions

References

Lorenz’ Butterfly

The Lorenz Equations

The Butterfly

Python Program

References

The Physics of Life, the Universe and Everything:

Read more about the history of chaos theory in Galileo Unbound from Oxford University Press

Stability of the Solar System

Poincaré and the King Oscar Prize of 1889

Chaos in the Poincaré Return Map

Chirikov’s Standard Map

The Physics of Life, the Universe and Everything:

Read more about the history of chaos theory in Galileo Unbound from Oxford University Press

Python Code: StandmapHom.py

References