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 his 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…

Poincaré’s first view of chaos.

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.

Python Code

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
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


eps = 0.97



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


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)


[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.

Timelines in the History and Physics of Dynamics (with links to primary texts)

These timelines in the History of Dynamics are organized along the Chapters in Galileo Unbound (Oxford, 2018). The book is about the physics and history of dynamics including classical and quantum mechanics as well as general relativity and nonlinear dynamics (with a detour down evolutionary dynamics and game theory along the way). The first few chapters focus on Galileo, while the following chapters follow his legacy, as theories of motion became more abstract, eventually to encompass the evolution of species within the same theoretical framework as the orbit of photons around black holes.

Galileo: A New Scientist

Galileo Galilei was the first modern scientist, launching a new scientific method that superseded, after one and a half millennia, Aristotle’s physics.  Galileo’s career began with his studies of motion at the University of Pisa that were interrupted by his move to the University of Padua and his telescopic discoveries of mountains on the moon and the moons of Jupiter.  Galileo became the first rock star of science, and he used his fame to promote the ideas of Copernicus and the Sun-centered model of the solar system.  But he pushed too far when he lampooned the Pope.  Ironically, Galileo’s conviction for heresy and his sentence to house arrest for the remainder of his life gave him the free time to finally finish his work on the physics of motion, which he published in Two New Sciences in 1638.

1543 Copernicus dies, publishes posthumously De Revolutionibus

1564    Galileo born

1581    Enters University of Pisa

1585    Leaves Pisa without a degree

1586    Invents hydrostatic balance

1588    Receives lecturship in mathematics at Pisa

1592    Chair of mathematics at Univeristy of Padua

1595    Theory of the tides

1595    Invents military and geometric compass

1596    Le Meccaniche and the principle of horizontal inertia

1600    Bruno Giordano burned at the stake

1601    Death of Tycho Brahe

1609    Galileo constructs his first telescope, makes observations of the moon

1610    Galileo discovers 4 moons of Jupiter, Starry Messenger (Sidereus Nuncius), appointed chief philosopher and mathematician of the Duke of Tuscany, moves to Florence, observes Saturn, Venus goes through phases like the moon

1611    Galileo travels to Rome, inducted into the Lyncean Academy, name “telescope” is first used

1611    Scheiner discovers sunspots

1611    Galileo meets Barberini, a cardinal

1611 Johannes Kepler, Dioptrice

1613    Letters on sunspots published by Lincean Academy in Rome

1614    Galileo denounced from the pulpit

1615    (April) Bellarmine writes an essay against Coperinicus

1615    Galileo investigated by the Inquisition

1615    Writes Letter to Christina, but does not publish it

1615    (December) travels to Rome and stays at Tuscan embassy

1616    (January) Francesco Ingoli publishes essay against Copernicus

1616    (March) Decree against copernicanism

1616    Galileo publishes theory of tides, Galileo meets with Pope Paul V, Copernicus’ book is banned, Galileo warned not to support the Coperinican system, Galileo decides not to reply to Ingoli, Galileo proposes eclipses of Jupter’s moons to determine longitude at sea

1618    Three comets appear, Grassi gives a lecture not hostile to Galileo

1618    Galileo, through Mario Guiducci, publishes scathing attack on Grassi

1619    Jesuit Grassi (Sarsi) publishes attack on Galileo concerning 3 comets

1619    Marina Gamba dies, Galileo legitimizes his son Vinczenzio

1619 Kepler’s Laws, Epitome astronomiae Copernicanae.

1623    Barberini becomes Urban VIII, The Assayer published (response to Grassi)

1624    Galileo visits Rome and Urban VIII

1629    Birth of his grandson Galileo

1630    Death of Johanes Kepler

1632    Publication of the Dialogue Concerning the Two Chief World Systems, Galileo is indicted by the Inquisition (68 years old)

1633    (February) Travels to Rome

1633    Convicted, abjurs, house arrest in Rome, then Siena, then home to Arcetri

1638    Blind, publication of Two New Sciences

1642    Galileo dies (77 years old)

Galileo’s Trajectory

Galileo’s discovery of the law of fall and the parabolic trajectory began with early work on the physics of motion by predecessors like the Oxford Scholars, Tartaglia and the polymath Simon Stevin who dropped lead weights from the leaning tower of Delft three years before Galileo (may have) dropped lead weights from the leaning tower of Pisa.  The story of how Galileo developed his ideas of motion is described in the context of his studies of balls rolling on inclined plane and the surprising accuracy he achieved without access to modern timekeeping.

1583    Galileo Notices isochronism of the pendulum

1588    Receives lecturship in mathematics at Pisa

1589 – 1592  Work on projectile motion in Pisa

1592    Chair of mathematics at Univeristy of Padua

1596    Le Meccaniche and the principle of horizontal inertia

1600    Guidobaldo shares technique of colored ball

1602    Proves isochronism of the pendulum (experimentally)

1604    First experiments on uniformly accelerated motion

1604    Wrote to Scarpi about the law of fall (s ≈ t2)

1607-1608  Identified trajectory as parabolic

1609    Velocity proportional to time

1632    Publication of the Dialogue Concerning the Two Chief World Systems, Galileo is indicted by the Inquisition (68 years old)

1636    Letter to Christina published in Augsburg in Latin and Italian

1638    Blind, publication of Two New Sciences

1641    Invented pendulum clock (in theory)

1642    Dies (77 years old)

On the Shoulders of Giants

Galileo’s parabolic trajectory launched a new approach to physics that was taken up by a new generation of scientists like Isaac Newton, Robert Hooke and Edmund Halley.  The English Newtonian tradition was adopted by ambitious French iconoclasts who championed Newton over their own Descartes.  Chief among these was Pierre Maupertuis, whose principle of least action was developed by Leonhard Euler and Joseph Lagrange into a rigorous new science of dynamics.  Along the way, Maupertuis became embroiled in a famous dispute that entangled the King of Prussia as well as the volatile Voltaire who was mourning the death of his mistress Emilie du Chatelet, the lone female French physicist of the eighteenth century.

1644    Descartes’ vortex theory of gravitation

1662    Fermat’s principle

1669 – 1690    Huygens expands on Descartes’ vortex theory

1687 Newton’s Principia

1698    Maupertuis born

1729    Maupertuis entered University in Basel.  Studied under Johann Bernoulli

1736    Euler publishes Mechanica sive motus scientia analytice exposita

1737   Maupertuis report on expedition to Lapland.  Earth is oblate.  Attacks Cassini.

1744    Maupertuis Principle of Least Action.  Euler Principle of Least Action.

1745    Maupertuis becomes president of Berlin Academy.  Paris Academy cancels his membership after a campaign against him by Cassini.

1746    Maupertuis principle of Least Action for mass

1751    Samuel König disputes Maupertuis’ priority

1756    Cassini dies.  Maupertuis reinstated in the French Academy

1759    Maupertuis dies

1759    du Chatelet’s French translation of Newton’s Principia published posthumously

1760    Euler 3-body problem (two fixed centers and coplanar third body)

1760-1761 Lagrange, Variational calculus (J. L. Lagrange, “Essai d’une nouvelle méthod pour dEeterminer les maxima et lest minima des formules intégrales indéfinies,” Miscellanea Teurinensia, (1760-1761))

1762    Beginning of the reign of Catherine the Great of Russia

1763    Euler colinear 3-body problem

1765    Euler publishes Theoria motus corporum solidorum on rotational mechanics

1766    Euler returns to St. Petersburg

1766    Lagrange arrives in Berlin

1772    Lagrange equilateral 3-body problem, Essai sur le problème des trois corps, 1772, Oeuvres tome 6

1775    Beginning of the American War of Independence

1776    Adam Smith Wealth of Nations

1781    William Herschel discovers Uranus

1783    Euler dies in St. Petersburg

1787    United States Constitution written

1787    Lagrange moves from Berlin to Paris

1788    Lagrange, Méchanique analytique

1789    Beginning of the French Revolution

1799    Pierre-Simon Laplace Mécanique Céleste (1799-1825)

Geometry on My Mind

This history of modern geometry focuses on the topics that provided the foundation for the new visualization of physics.  It begins with Carl Gauss and Bernhard Riemann, who redefined geometry and identified the importance of curvature for physics.  Vector spaces, developed by Hermann Grassmann, Giuseppe Peano and David Hilbert, are examples of the kinds of abstract new spaces that are so important for modern physics, such as Hilbert space for quantum mechanics.  Fractal geometry developed by Felix Hausdorff later provided the geometric language needed to solve problems in chaos theory.

1629    Fermat described higher-dim loci

1637    Descarte’s Geometry

1649    van Schooten’s commentary on Descartes Geometry

1694    Leibniz uses word “coordinate” in its modern usage

1697    Johann Bernoulli shortest distance between two points on convex surface

1732    Euler geodesic equations for implicit surfaces

1748    Euler defines modern usage of function

1801    Gauss calculates orbit of Ceres

1807    Fourier analysis (published in 1822(

1807    Gauss arrives in Göttingen

1827    Karl Gauss establishes differential geometry of curved surfaces, Disquisitiones generales circa superficies curvas

1830    Bolyai and Lobachevsky publish on hyperbolic geometry

1834    Jacobi n-fold integrals and volumes of n-dim spheres

1836    Liouville-Sturm theorem

1838    Liouville’s theorem

1841    Jacobi determinants

1843    Arthur Cayley systems of n-variables

1843    Hamilton discovers quaternions

1844    Hermann Grassman n-dim vector spaces, Die Lineale Ausdehnungslehr

1846    Julius Plücker System der Geometrie des Raumes in neuer analytischer Behandlungsweise

1848 Jacobi Vorlesungen über Dynamik

1848    “Vector” coined by Hamilton

1854    Riemann’s habilitation lecture

1861    Riemann n-dim solution of heat conduction

1868    Publication of Riemann’s Habilitation

1869    Christoffel and Lipschitz work on multiple dimensional analysis

1871    Betti refers to the n-ply of numbers as a “space”.

1871    Klein publishes on non-euclidean geometry

1872 Boltzmann distribution

1872    Jordan Essay on the geometry of n-dimensions

1872    Felix Klein’s “Erlangen Programme”

1872    Weierstrass’ Monster

1872    Dedekind cut

1872    Cantor paper on irrational numbers

1872    Cantor meets Dedekind

1872 Lipschitz derives mechanical motion as a geodesic on a manifold

1874    Cantor beginning of set theory

1877    Cantor one-to-one correspondence between the line and n-dimensional space

1881    Gibbs codifies vector analysis

1883    Cantor set and staircase Grundlagen einer allgemeinen Mannigfaltigkeitslehre

1884    Abbott publishes Flatland

1887    Peano vector methods in differential geometry

1890    Peano space filling curve

1891    Hilbert space filling curve

1887    Darboux vol. 2 treats dynamics as a point in d-dimensional space.  Applies concepts of geodesics for trajectories.

1898    Ricci-Curbastro Lesons on the Theory of Surfaces

1902    Lebesgue integral

1904    Hilbert studies integral equations

1904    von Koch snowflake

1906    Frechet thesis on square summable sequences as infinite dimensional space

1908    Schmidt Geometry in a Function Space

1910    Brouwer proof of dimensional invariance

1913    Hilbert space named by Riesz

1914    Hilbert space used by Hausdorff

1915    Sierpinski fractal triangle

1918    Hausdorff non-integer dimensions

1918    Weyl’s book Space, Time, Matter

1918    Fatou and Julia fractals

1920    Banach space

1927    von Neumann axiomatic form of Hilbert Space

1935    Frechet full form of Hilbert Space

1967    Mandelbrot coast of Britain

1982    Mandelbrot’s book The Fractal Geometry of Nature

The Tangled Tale of Phase Space

Phase space is the central visualization tool used today to study complex systems.  The chapter describes the origins of phase space with the work of Joseph Liouville and Carl Jacobi that was later refined by Ludwig Boltzmann and Rudolf Clausius in their attempts to define and explain the subtle concept of entropy.  The turning point in the history of phase space was when Henri Poincaré used phase space to solve the three-body problem, uncovering chaotic behavior in his quest to answer questions on the stability of the solar system.  Phase space was established as the central paradigm of statistical mechanics by JW Gibbs and Paul Ehrenfest.

1804    Jacobi born (1904 – 1851) in Potsdam

1804    Napoleon I Emperor of France

1806    William Rowan Hamilton born (1805 – 1865)

1807    Thomas Young describes “Energy” in his Course on Natural Philosophy (Vol. 1 and Vol. 2)

1808    Bethoven performs his Fifth Symphony

1809    Joseph Liouville born (1809 – 1882)

1821    Hermann Ludwig Ferdinand von Helmholtz born (1821 – 1894)

1824    Carnot published Reflections on the Motive Power of Fire

1834    Jacobi n-fold integrals and volumes of n-dim spheres

1834-1835       Hamilton publishes his principle (1834, 1835).

1836    Liouville-Sturm theorem

1837    Queen Victoria begins her reign as Queen of England

1838    Liouville develops his theorem on products of n differentials satisfying certain first-order differential equations.  This becomes the classic reference to Liouville’s Theorem.

1847    Helmholtz  Conservation of Energy (force)

1849    Thomson makes first use of “Energy” (From reading Thomas Young’s lecture notes)

1850    Clausius establishes First law of Thermodynamics: Internal energy. Second law:  Heat cannot flow unaided from cold to hot.  Not explicitly stated as first and second laws

1851    Thomson names Clausius’ First and Second laws of Thermodynamics

1852    Thomson describes general dissipation of the universe (“energy” used in title)

1854    Thomson defined absolute temperature.  First mathematical statement of 2nd law.  Restricted to reversible processes

1854    Clausius stated Second Law of Thermodynamics as inequality

1857    Clausius constructs kinetic theory, Mean molecular speeds

1858    Clausius defines mean free path, Molecules have finite size. Clausius assumed that all molecules had the same speed

1860    Maxwell publishes first paper on kinetic theory. Distribution of speeds. Derivation of gas transport properties

1865    Loschmidt size of molecules

1865    Clausius names entropy

1868    Boltzmann adds (Boltzmann) factor to Maxwell distribution

1872    Boltzmann transport equation and H-theorem

1876    Loschmidt reversibility paradox

1877    Boltzmann  S = k logW

1890    Poincare: Recurrence Theorem. Recurrence paradox with Second Law (1893)

1896    Zermelo criticizes Boltzmann

1896    Boltzmann posits direction of time to save his H-theorem

1898    Boltzmann Vorlesungen über Gas Theorie

1905    Boltzmann kinetic theory of matter in Encyklopädie der mathematischen Wissenschaften

1906    Boltzmann dies

1910    Paul Hertz uses “Phase Space” (Phasenraum)

1911    Ehrenfest’s article in Encyklopädie der mathematischen Wissenschaften

1913    A. Rosenthal writes the first paper using the phrase “phasenraum”, combining the work of Boltzmann and Poincaré. “Beweis der Unmöglichkeit ergodischer Gassysteme” (Ann. D. Physik, 42, 796 (1913)

1913    Plancheral, “Beweis der Unmöglichkeit ergodischer mechanischer Systeme” (Ann. D. Physik, 42, 1061 (1913).  Also uses “Phasenraum”.

The Lens of Gravity

Gravity provided the backdrop for one of the most important paradigm shifts in the history of physics.  Prior to Albert Einstein’s general theory of relativity, trajectories were paths described by geometry.  After the theory of general relativity, trajectories are paths caused by geometry.  This chapter explains how Einstein arrived at his theory of gravity, relying on the space-time geometry of Hermann Minkowski, whose work he had originally harshly criticized.  The confirmation of Einstein’s theory was one of the dramatic high points in 20th century history of physics when Arthur Eddington journeyed to an island off the coast of Africa to observe stellar deflections during a solar eclipse.  If Galileo was the first rock star of physics, then Einstein was the first worldwide rock star of science.

1697    Johann Bernoulli was first to find solution to shortest path between two points on a curved surface (1697).

1728    Euler found the geodesic equation.

1783    The pair 40 Eridani B/C was discovered by William Herschel on 31 January

1783    John Michell explains infalling object would travel faster than speed of light

1796    Laplace describes “dark stars” in Exposition du system du Monde

1827    The first orbit of a binary star computed by Félix Savary for the orbit of Xi Ursae Majoris.

1827    Gauss curvature Theoriem Egregum

1844    Bessel notices periodic displacement of Sirius with period of half a century

1844    The name “geodesic line” is attributed to Liouville.

1845    Buys Ballot used musicians with absolute pitch for the first experimental verification of the Doppler effect

1854    Riemann’s habilitationsschrift

1862    Discovery of Sirius B (a white dwarf)

1868    Darboux suggested motions in n-dimensions

1872    Lipshitz first to apply Riemannian geometry to the principle of least action.

1895    Hilbert arrives in Göttingen

1902    Minkowski arrives in Göttingen

1905    Einstein’s miracle year

1906    Poincaré describes Lorentz transformations as rotations in 4D

1907    Einstein has “happiest thought” in November

1907    Einstein’s relativity review in Jahrbuch

1908    Minkowski’s Space and Time lecture

1908    Einstein appointed to unpaid position at University of Bern

1909    Minkowski dies

1909    Einstein appointed associate professor of theoretical physics at U of Zürich

1910    40 Eridani B was discobered to be of spectral type A (white dwarf)

1910    Size and mass of Sirius B determined (heavy and small)

1911    Laue publishes first textbook on relativity theory

1911    Einstein accepts position at Prague

1911    Einstein goes to the limits of special relativity applied to gravitational fields

1912    Einstein’s two papers establish a scalar field theory of gravitation

1912    Einstein moves from Prague to ETH in Zürich in fall.  Begins collaboration with Grossmann.

1913    Einstein EG paper

1914    Adams publishes spectrum of 40 Eridani B

1915    Sirius B determined to be also a low-luminosity type A white dwarf

1915    Einstein Completes paper

1916    Density of 40 Eridani B by Ernst Öpik

1916    Schwarzschild paper

1916 Einstein’s publishes theory of gravitational waves

1919    Eddington expedition to Principe

1920    Eddington paper on deflection of light by the sun

1922    Willem Luyten coins phrase “white dwarf”

1924    Eddington found a set of coordinates that eliminated the singularity at the Schwarzschild radius

1926    R. H. Fowler publishes paper on degenerate matter and composition of white dwarfs

1931    Chandrasekhar calculated the limit for collapse to white dwarf stars at 1.4MS

1933    Georges Lemaitre states the coordinate singularity was an artefact

1934    Walter Baade and Fritz Zwicky proposed the existence of the neutron star only a year after the discovery of the neutron by Sir James Chadwick.

1939    Oppenheimer and Snyder showed ultimate collapse of a 3MS  “frozen star”

1958    David Finkelstein paper

1965    Antony Hewish and Samuel Okoye discovered “an unusual source of high radio brightness temperature in the Crab Nebula”. This source turned out to be the Crab Nebula neutron star that resulted from the great supernova of 1054.

1967    Jocelyn Bell and Antony Hewish discovered regular radio pulses from CP 1919. This pulsar was later interpreted as an isolated, rotating neutron star.

1967    Wheeler’s “black hole” talk

1974    Joseph Taylor and Russell Hulse discovered the first binary pulsar, PSR B1913+16, which consists of two neutron stars (one seen as a pulsar) orbiting around their center of mass.

2015    LIGO detects gravitational waves on Sept. 14 from the merger of two black holes

2017    LIGO detects the merger of two neutron stars

On the Quantum Footpath

The concept of the trajectory of a quantum particle almost vanished in the battle between Werner Heisenberg’s matrix mechanics and Erwin Schrödinger’s wave mechanics.  It took Niels Bohr and his complementarity principle of wave-particle duality to cede back some reality to quantum trajectories.  However, Schrödinger and Einstein were not convinced and conceived of quantum entanglement to refute the growing acceptance of the Copenhagen Interpretation of quantum physics.  Schrödinger’s cat was meant to be an absurdity, but ironically it has become a central paradigm of practical quantum computers.  Quantum trajectories took on new meaning when Richard Feynman constructed quantum theory based on the principle of least action, inventing his famous Feynman Diagrams to help explain quantum electrodynamics.

1885    Balmer Theory: 

1897    J. J. Thomson discovered the electron

1904    Thomson plum pudding model of the atom

1911    Bohr PhD thesis filed. Studies on the electron theory of metals.  Visited England.

1911    Rutherford nuclear model

1911    First Solvay conference

1911    “ultraviolet catastrophe” coined by Ehrenfest

1913    Bohr combined Rutherford’s nuclear atom with Planck’s quantum hypothesis: 1913 Bohr model

1913    Ehrenfest adiabatic hypothesis

1914-1916       Bohr at Manchester with Rutherford

1916    Bohr appointed Chair of Theoretical Physics at University of Copenhagen: a position that was made just for him

1916    Schwarzschild and Epstein introduce action-angle coordinates into quantum theory

1920    Heisenberg enters University of Munich to obtain his doctorate

1920    Bohr’s Correspondence principle: Classical physics for large quantum numbers

1921    Bohr Founded Institute of Theoretical Physics (Copenhagen)

1922-1923       Heisenberg studies with Born, Franck and Hilbert at Göttingen while Sommerfeld is in the US on sabbatical.

1923    Heisenberg Doctorate.  The exam does not go well.  Unable to derive the resolving power of a microscope in response to question by Wien.  Becomes Born’s assistant at Göttingen.

1924    Heisenberg visits Niels Bohr in Copenhagen (and met Einstein?)

1924    Heisenberg Habilitation at Göttingen on anomalous Zeeman

1924 – 1925    Heisenberg worked with Bohr in Copenhagen, returned summer of 1925 to Göttiingen

1924    Pauli exclusion principle and state occupancy

1924    de Broglie hypothesis extended wave-particle duality to matter

1924    Bohr Predicted Halfnium (72)

1924    Kronig’s proposal for electron self spin

1924    Bose (Einstein)

1925    Heisenberg paper on quantum mechanics

1925    Dirac, reading proof from Heisenberg, recognized the analogy of noncommutativity with Poisson brackets and the correspondence with Hamiltonian mechanics.

1925    Uhlenbeck and Goudschmidt: spin

1926    Born, Heisenberg, Kramers: virtual oscillators at transition frequencies: Matrix mechanics (alternative to Bohr-Kramers-Slater 1924 model of orbits).  Heisenberg was Born’s student at Göttingen.

1926    Schrödinger wave mechanics

1927    de Broglie hypotehsis confirmed by Davisson and Germer

1927    Complementarity by Bohr: wave-particle duality “Evidence obtained under different experimental conditions cannot be comprehended within a single picture, but must be regarded as complementary in the sense that only the totality of the phenomena exhausts the possible information about the objects.

1927    Heisenberg uncertainty principle (Heisenberg was in Copenhagen 1926 – 1927)

1927    Solvay Conference in Brussels

1928    Heisenberg to University of Leipzig

1928    Dirac relativistic QM equation

1929    de Broglie Nobel Prize

1930    Solvay Conference

1932    Heisenberg Nobel Prize

1932    von Neumann operator algebra

1933    Dirac Lagrangian form of QM (basis of Feynman path integral)

1933    Schrödinger and Dirac Nobel Prize

1935    Einstein, Poldolsky and Rosen EPR paper

1935 Bohr’s response to Einsteins “EPR” paradox

1935    Schrodinger’s cat

1939    Feynman graduates from MIT

1941    Heisenberg (head of German atomic project) visits Bohr in Copenhagen

1942    Feynman PhD at Princeton, “The Principle of Least Action in Quantum Mechanics

1942 – 1945    Manhattan Project, Bethe-Feynman equation for fission yield

1943    Bohr escapes to Sweden in a fishing boat.  Went on to England secretly.

1945    Pauli Nobel Prize

1945    Death of Feynman’s wife Arline (married 4 years)

1945    Fall, Feynman arrives at Cornell ahead of Hans Bethe

1947    Shelter Island conference: Lamb Shift, did Kramer’s give a talk suggesting that infinities could be subtracted?

1947    Fall, Dyson arrives at Cornell

1948    Pocono Manor, Pennsylvania, troubled unveiling of path integral formulation and Feynman diagrams, Schwinger’s master presentation

1948    Feynman and Dirac. Summer drive across the US with Dyson

1949    Dyson joins IAS as a postdoc, trains a cohort of theorists in Feynman’s technique

1949    Karplus and Kroll first g-factor calculation

1950    Feynman moves to Cal Tech

1965    Schwinger, Tomonaga and Feynman Nobel Prize

1967    Hans Bethe Nobel Prize

From Butterflies to Hurricanes

Half a century after Poincaré first glimpsed chaos in the three-body problem, the great Russian mathematician Andrey Kolmogorov presented a sketch of a theorem that could prove that orbits are stable.  In the hands of Vladimir Arnold and Jürgen Moser, this became the KAM theory of Hamiltonian chaos.  This chapter shows how KAM theory fed into topology in the hands of Stephen Smale and helped launch the new field of chaos theory.  Edward Lorenz discovered chaos in numerical models of atmospheric weather and discovered the eponymous strange attractor.  Mathematical aspects of chaos were further developed by Mitchell Feigenbaum studying bifurcations in the logistic map that describes population dynamics.

1760    Euler 3-body problem (two fixed centers and coplanar third body)

1763    Euler colinear 3-body problem

1772    Lagrange equilateral 3-body problem

1881-1886       Poincare memoires “Sur les courbes de ́finies par une equation differentielle”

1890    Poincare “Sur le probleme des trois corps et les equations de la dynamique”. First-return map, Poincare recurrence theorem, stable and unstable manifolds

1892 – 1899    Poincare New Methods in Celestial Mechanics

1892    Lyapunov The General Problem of the Stability of Motion

1899    Poincare homoclinic trajectory

1913    Birkhoff proves Poincaré’s last geometric theorem, a special case of the three-body problem.

1927    van der Pol and van der Mark

1937    Coarse systems, Andronov and Pontryagin

1938    Morse theory

1942    Hopf bifurcation

1945    Cartwright and Littlewood study the van der Pol equation (Radar during WWII)

1954    Kolmogorov A. N., On conservation of conditionally periodic motions for a small change in Hamilton’s function.

1960    Lorenz: 12 equations

1962    Moser On Invariant Curves of Area-Preserving Mappings of an Annulus.

1963    Arnold Small denominators and problems of the stability of motion in classical and celestial mechanics

1963    Lorenz: 3 equations

1964    Arnold diffusion

1965    Smale’s horseshoe

1969    Chirikov standard map

1971    Ruelle-Takens (Ruelle coins phrase “strange attractor”)

1972    “Butterfly Effect” given for Lorenz’ talk (by Philip Merilees)

1975    Gollub-Swinney observe route to turbulence along lines of Ruelle

1975    Yorke coins “chaos theory”

1976    Robert May writes review article of the logistic map

1977    New York conference on bifurcation theory

1987    James Gleick Chaos: Making a New Science

Darwin in the Clockworks

The preceding timelines related to the central role played by families of trajectories phase space to explain the time evolution of complex systems.  These ideas are extended to explore the history and development of the theory of natural evolution by Charles Darwin.  Darwin had many influences, including ideas from Thomas Malthus in the context of economic dynamics.  After Darwin, the ideas of evolution matured to encompass broad topics in evolutionary dynamics and the emergence of the idea of fitness landscapes and game theory driving the origin of new species.  The rise of genetics with Gregor Mendel supplied a firm foundation for molecular evolution, leading to the moleculer clock of Linus Pauling and the replicator dynamics of Richard Dawkins.

1202    Fibonacci

1766    Thomas Robert Malthus born

1776    Adam Smith The Wealth of Nations

1798    Malthus “An Essay on the Principle of Population

1817    Ricardo Principles of Political Economy and Taxation

1838    Cournot early equilibrium theory in duopoly

1848    John Stuart Mill

1848    Karl Marx Communist Manifesto

1859    Darwin Origin of Species

1867    Karl Marx Das Kapital

1871    Darwin Descent of Man, and Selection in Relation to Sex

1871    Jevons Theory of Political Economy

1871    Menger Principles of Economics

1874    Walrus Éléments d’économie politique pure, or Elements of Pure Economics (1954)

1890    Marshall Principles of Economics

1908    Hardy constant genetic variance

1910    Brouwer fixed point theorem

1910    Alfred J. Lotka autocatylitic chemical reactions

1913    Zermelo determinancy in chess

1922    Fisher dominance ratio

1922    Fisher mutations

1925    Lotka predator-prey in biomathematics

1926    Vita Volterra published same equations independently

1927    JBS Haldane (1892—1964) mutations

1928    von Neumann proves the minimax theorem

1930    Fisher ratio of sexes

1932    Wright Adaptive Landscape

1932    Haldane The Causes of Evolution

1933    Kolmogorov Foundations of the Theory of Probability

1934    Rudolph Carnap The Logical Syntax of Language

1936    John Maynard Keynes, The General Theory of Employment, Interest and Money

1936    Kolmogorov generalized predator-prey systems

1938    Borel symmetric payoff matrix

1942    Sewall Wright    Statistical Genetics and Evolution

1943    McCulloch and Pitts A Logical Calculus of Ideas Immanent in Nervous Activity

1944    von Neumann and Morgenstern Theory of Games and Economic Behavior

1950    Prisoner’s Dilemma simulated at Rand Corportation

1950    John Nash Equilibrium points in n-person games and The Bargaining Problem

1951    John Nash Non-cooperative Games

1952    McKinsey Introduction to the Theory of Games (first textbook)

1953    John Nash Two-Person Cooperative Games

1953    Watson and Crick DNA

1955    Braithwaite’s Theory of Games as a Tool for the Moral Philosopher

1961    Lewontin Evolution and the Theory of Games

1962    Patrick Moran The Statistical Processes of Evolutionary Theory

1962    Linus Pauling molecular clock

1968    Motoo Kimura  neutral theory of molecular evolution

1972    Maynard Smith introduces the evolutionary stable solution (ESS)

1972    Gould and Eldridge Punctuated equilibrium

1973    Maynard Smith and Price The Logic of Animal Conflict

1973    Black Scholes

1977    Eigen and Schuster The Hypercycle

1978    Replicator equation (Taylor and Jonker)

1982    Hopfield network

1982    John Maynard Smith Evolution and the Theory of Games

1984    R. Axelrod The Evolution of Cooperation

The Measure of Life

This final topic extends the ideas of dynamics into abstract spaces of high dimension to encompass the idea of a trajectory of life.  Health and disease become dynamical systems defined by all the proteins and nucleic acids that comprise the physical self.  Concepts from network theory, autonomous oscillators and synchronization contribute to this viewpoint.  Healthy trajectories are like stable limit cycles in phase space, but disease can knock the system trajectory into dangerous regions of health space, as doctors turn to new developments in personalized medicine try to return the individual to a healthy path.  This is the ultimate generalization of Galileo’s simple parabolic trajectory.

1642    Galileo dies

1656    Huygens invents pendulum clock

1665    Huygens observes “odd kind of sympathy” in synchronized clocks

1673    Huygens publishes Horologium Oscillatorium sive de motu pendulorum

1736    Euler Seven Bridges of Königsberg

1845    Kirchhoff’s circuit laws

1852    Guthrie four color problem

1857    Cayley trees

1858    Hamiltonian cycles

1887    Cajal neural staining microscopy

1913    Michaelis Menten dynamics of enzymes

1924    Berger, Hans: neural oscillations (Berger invented the EEG)

1926    van der Pol dimensioness form of equation

1927    van der Pol periodic forcing

1943    McCulloch and Pits mathematical model of neural nets

1948    Wiener cybernetics

1952    Hodgkin and Huxley action potential model

1952    Turing instability model

1956    Sutherland cyclic AMP

1957    Broadbent and Hammersley bond percolation

1958    Rosenblatt perceptron

1959    Erdös and Renyi random graphs

1962    Cohen EGF discovered

1965    Sebeok coined zoosemiotics

1966    Mesarovich systems biology

1967    Winfree biological rythms and coupled oscillators

1969    Glass Moire patterns in perception

1970    Rodbell G-protein

1971    phrase “strange attractor” coined (Ruelle)

1972    phrase “signal transduction” coined (Rensing)

1975    phrase “chaos theory” coined (Yorke)

1975    Werbos backpropagation

1975    Kuramoto transition

1976    Robert May logistic map

1977    Mackey-Glass equation and dynamical disease

1982    Hopfield network

1990    Strogatz and Murillo pulse-coupled oscillators

1997    Tomita systems biology of a cell

1998    Strogatz and Watts Small World network

1999    Barabasi Scale Free networks

2000    Sequencing of the human genome

Second Edition of Introduction to Modern Dynamics (Chaos, Networks, Space and Time)

The second edition of Introduction to Modern Dynamics: Chaos, Networks, Space and Time is available from Oxford University Press and Amazon.

Most physics majors will use modern dynamics in their careers: nonlinearity, chaos, network theory, econophysics, game theory, neural nets, geodesic geometry, among many others.

The first edition of Introduction to Modern Dynamics (IMD) was an upper-division junior-level mechanics textbook at the level of Thornton and Marion (Classical Dynamics of Particles and Systems) and Taylor (Classical Mechanics).  IMD helped lead an emerging trend in physics education to update the undergraduate physics curriculum.  Conventional junior-level mechanics courses emphasized Lagrangian and Hamiltonian physics, but notably missing from the classic subjects are modern dynamics topics that most physics majors will use in their careers: nonlinearity, chaos, network theory, econophysics, game theory, neural nets, geodesic geometry, among many others.  These are the topics at the forefront of physics that drive high-tech businesses and start-ups, which is where more than half of all physicists work. IMD introduced these modern topics to junior-level physics majors in an accessible form that allowed them to master the fundamentals to prepare them for the modern world.

The second edition (IMD2) continues that trend by expanding the chapters to include additional material and topics.  It rearranges several of the introductory chapters for improved logical flow and expands them to include key conventional topics that were missing in the first edition (e.g., Lagrange undetermined multipliers and expanded examples of Lagrangian applications).  It is also an opportunity to correct several typographical errors and other errata that students have identified over the past several years.  The second edition also has expanded homework problems.

The goal of IMD2 is to strengthen the sections on conventional topics (that students need to master to take their GREs) to make IMD2 attractive as a mainstream physics textbook for broader adoption at the junior level, while continuing the program of updating the topics and approaches that are relevant for the roles that physicists play in the 21st century.

(New Chapters and Sections highlighted in red.)

New Features in Second Edition:

Second Edition Chapters and Sections

Part 1 Geometric Mechanics

• Expanded development of Lagrangian dynamics

• Lagrange multipliers

• More examples of applications

• Connection to statistical mechanics through the virial theorem

• Greater emphasis on action-angle variables

• The key role of adiabatic invariants

Part 1 Geometric Mechanics

Chapter 1 Physics and Geometry

1.1 State space and dynamical flows

1.2 Coordinate representations

1.3 Coordinate transformation

1.4 Uniformly rotating frames

1.5 Rigid-body motion

Chapter 2 Lagrangian Mechanics

2.1 Calculus of variations

2.2 Lagrangian applications

2.3 Lagrange’s undetermined multipliers

2.4 Conservation laws

2.5 Central force motion

2.6 Virial Theorem

Chapter 3 Hamiltonian Dynamics and Phase Space

3.1 The Hamiltonian function

3.2 Phase space

3.3 Integrable systems and action–angle variables

3.4 Adiabatic invariants

Part 2 Nonlinear Dynamics

• New section on non-autonomous dynamics

• Entire new chapter devoted to Hamiltonian mechanics

• Added importance to Chirikov standard map

• The important KAM theory of “constrained chaos” and solar system stability

• Degeneracy in Hamiltonian chaos

• A short overview of quantum chaos

• Rational resonances and the relation to KAM theory

• Synchronized chaos

Part 2 Nonlinear Dynamics

Chapter 4 Nonlinear Dynamics and Chaos

4.1 One-variable dynamical systems

4.2 Two-variable dynamical systems

4.3 Limit cycles

4.4 Discrete iterative maps

4.5 Three-dimensional state space and chaos

4.6 Non-autonomous (driven) flows

4.7 Fractals and strange attractors

Chapter 5 Hamiltonian Chaos

5.1 Perturbed Hamiltonian systems

5.2 Nonintegrable Hamiltonian systems

5.3 The Chirikov Standard Map

5.4 KAM Theory

5.5 Degeneracy and the web map

5.6 Quantum chaos

Chapter 6 Coupled Oscillators and Synchronization

6.1 Coupled linear oscillators

6.2 Simple models of synchronization

6.3 Rational resonances

6.4 External synchronization

6.5 Synchronization of Chaos

Part 3 Complex Systems

• New emphasis on diffusion on networks

• Epidemic growth on networks

• A new section of game theory in the context of evolutionary dynamics

• A new section on general equilibrium theory in economics

Part 3 Complex Systems

Chapter 7 Network Dynamics

7.1 Network structures

7.2 Random network topologies

7.3 Synchronization on networks

7.4 Diffusion on networks

7.5 Epidemics on networks

Chapter 8 Evolutionary Dynamics

81 Population dynamics

8.2 Virus infection and immune deficiency

8.3 Replicator Dynamics

8.4 Quasi-species

8.5 Game theory and evolutionary stable solutions

Chapter 9 Neurodynamics and Neural Networks

9.1 Neuron structure and function

9.2 Neuron dynamics

9.3 Network nodes: artificial neurons

9.4 Neural network architectures

9.5 Hopfield neural network

9.6 Content-addressable (associative) memory

Chapter 10 Economic Dynamics

10.1 Microeconomics and equilibrium

10.2 Macroeconomics

10.3 Business cycles

10.4 Random walks and stock prices (optional)

Part 4 Relativity and Space–Time

• Relativistic trajectories

• Gravitational waves

Part 4 Relativity and Space–Time

Chapter 11 Metric Spaces and Geodesic Motion

11.1 Manifolds and metric tensors

11.2 Derivative of a tensor

11.3 Geodesic curves in configuration space

11.4 Geodesic motion

Chapter 12 Relativistic Dynamics

12.1 The special theory

12.2 Lorentz transformations

12.3 Metric structure of Minkowski space

12.4 Relativistic trajectories

12.5 Relativistic dynamics

12.6 Linearly accelerating frames (relativistic)

Chapter 13 The General Theory of Relativity and Gravitation

13.1 Riemann curvature tensor

13.2 The Newtonian correspondence

13.3 Einstein’s field equations

13.4 Schwarzschild space–time

13.5 Kinematic consequences of gravity

13.6 The deflection of light by gravity

13.7 The precession of Mercury’s perihelion

13.8 Orbits near a black hole

13.9 Gravitational waves

Synopsis of 2nd Ed. Chapters

Chapter 1. Physics and Geometry (Sample Chapter)

This chapter has been rearranged relative to the 1st edition to provide a more logical flow of the overarching concepts of geometric mechanics that guide the subsequent chapters.  The central role of coordinate transformations is strengthened, as is the material on rigid-body motion with expanded examples.

Chapter 2. Lagrangian Mechanics (Sample Chapter)

Much of the structure and material is retained from the 1st edition while adding two important sections.  The section on applications of Lagrangian mechanics adds many direct examples of the use of Lagrange’s equations of motion.  An additional new section covers the important topic of Lagrange’s undetermined multipliers

Chapter 3. Hamiltonian Dynamics and Phase Space (Sample Chapter)

The importance of Hamiltonian systems and dynamics merits a stand-alone chapter.  The topics from the 1st edition are expanded in this new chapter, including a new section on adiabatic invariants that plays an important role in the development of quantum theory.  Some topics are de-emphasized from the 1st edition, such as general canonical transformations and the symplectic structure of phase space, although the specific transformation to action-angle coordinates is retained and amplified.

Chapter 4. Nonlinear Dynamics and Chaos

The first part of this chapter is retained from the 1st edition with numerous minor corrections and updates of figures.  The second part of the IMD 1st edition, treating Hamiltonian chaos, will be expanded into the new Chapter 5.

Chapter 5. Hamiltonian Chaos

This new stand-alone chapter expands on the last half of Chapter 3 of the IMD 1st edition.  The physical character of Hamiltonian chaos is substantially distinct from dissipative chaos that it deserves its own chapter.  It is also a central topic of interest for complex systems that are either conservative or that have integral invariants, such as our N-body solar system that played such an important role in the history of chaos theory beginning with Poincaré.  The new chapter highlights Poincaré’s homoclinic tangle, illustrated by the Chirikov Standard Map.  The Standard Map is an excellent introduction to KAM theory, which is one of the crowning achievements of the theory of dynamical systems by Komogorov, Arnold and Moser, connecting to deeper aspects of synchronization and rational resonances that drive the structure of systems as diverse as the rotation of the Moon and the rings of Saturn.  This is also a perfect lead-in to the next chapter on synchronization.  An optional section at the end of this chapter briefly discusses quantum chaos to show how Hamiltonian chaos can be extended into the quantum regime.

Chapter 6. Synchronization

This is an updated version of the IMD 1st ed. chapter.  It has a reduced initial section on coupled linear oscillators, retaining the key ideas about linear eigenmodes but removing some irrelevant details in the 1st edition.  A new section is added that defines and emphasizes the importance of quasi-periodicity.  A new section on the synchronization of chaotic oscillators is added.

Chapter 7. Network Dynamics

This chapter rearranges the structure of the chapter from the 1st edition, moving synchronization on networks earlier to connect from the previous chapter.  The section on diffusion and epidemics is moved to the back of the chapter and expanded in the 2nd edition into two separate sections on these topics, adding new material on discrete matrix approaches to continuous dynamics.

Chapter 8. Neurodynamics and Neural Networks

This chapter is retained from the 1st edition with numerous minor corrections and updates of figures.

Chapter 9. Evolutionary Dynamics

Two new sections are added to this chapter.  A section on game theory and evolutionary stable solutions introduces core concepts of evolutionary dynamics that merge well with the other topics of the chapter such as the pay-off matrix and replicator dynamics.  A new section on nearly neutral networks introduces new types of behavior that occur in high-dimensional spaces which are counter intuitive but important for understanding evolutionary drift.

Chapter 10.  Economic Dynamics

This chapter will be significantly updated relative to the 1st edition.  Most of the sections will be rewritten with improved examples and figures.  Three new sections will be added.  The 1st edition section on consumer market competition will be split into two new sections describing the Cournot duopoly and Pareto optimality in one section, and Walras’ Law and general equilibrium theory in another section.  The concept of the Pareto frontier in economics is becoming an important part of biophysical approaches to population dynamics.  In addition, new trends in economics are drawing from general equilibrium theory, first introduced by Walras in the nineteenth century, but now merging with modern ideas of fixed points and stable and unstable manifolds.  A third new section is added on econophysics, highlighting the distinctions that contrast economic dynamics (phase space dynamical approaches to economics) from the emerging field of econophysics (statistical mechanics approaches to economics).

Chapter 11. Metric Spaces and Geodesic Motion

 This chapter is retained from the 1st edition with several minor corrections and updates of figures.

Chapter 12. Relativistic Dynamics

This chapter is retained from the 1st edition with minor corrections and updates of figures.  More examples will be added, such as invariant mass reconstruction.  The connection between relativistic acceleration and Einstein’s equivalence principle will be strengthened.

Chapter 13. The General Theory of Relativity and Gravitation

This chapter is retained from the 1st edition with minor corrections and updates of figures.  A new section will derive the properties of gravitational waves, given the spectacular success of LIGO and the new field of gravitational astronomy.

Homework Problems:

All chapters will have expanded and updated homework problems.  Many of the homework problems from the 1st edition will remain, but the number of problems at the end of each chapter will be nearly doubled, while removing some of the less interesting or problematic problems.


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

The Physics of Modern Dynamics (with Python Programs)

It is surprising how much of modern dynamics boils down to an extremely simple formula

This innocuous-looking equation carries such riddles, such surprises, such unintuitive behavior that it can become the object of study for life.  This equation is called a vector flow equation, and it can be used to capture the essential physics of economies, neurons, ecosystems, networks, and even orbits of photons around black holes.  This equation is to modern dynamics what F = ma was to classical mechanics.  It is the starting point for understanding complex systems.

The Magic of Phase Space

The apparent simplicity of the “flow equation” masks the complexity it contains.  It is a vector equation because each “dimension” is a variable of a complex system.  Many systems of interest may have only a few variables, but ecosystems and economies and social networks may have hundreds or thousands of variables.  Expressed in component format, the flow equation is

where the superscript spans the number of variables.  But even this masks all that can happen with such an equation. Each of the functions fa can be entirely different from each other, and can be any type of function, whether polynomial, rational, algebraic, transcendental or composite, although they must be single-valued.  They are generally nonlinear, and the limitless ways that functions can be nonlinear is where the richness of the flow equation comes from.

The vector flow equation is an ordinary differential equation (ODE) that can be solved for specific trajectories as initial value problems.  A single set of initial conditions defines a unique trajectory.  For instance, the trajectory for a 4-dimensional example is described as the column vector

which is the single-parameter position vector to a point in phase space, also called state space.  The point sweeps through successive configurations as a function of its single parameter—time.  This trajectory is also called an orbit.  In classical mechanics, the focus has tended to be on the behavior of specific orbits that arise from a specific set of initial conditions.  This is the classic “rock thrown from a cliff” problem of introductory physics courses.  However, in modern dynamics, the focus shifts away from individual trajectories to encompass the set of all possible trajectories.

Why is Modern Dynamics part of Physics?

If finding the solutions to the “x-dot equals f” vector flow equation is all there is to do, then this would just be a math problem—the solution of ODE’s.  There are plenty of gems for mathematicians to look for, and there is an entire of field of study in mathematics called “dynamical systems“, but this would not be “physics”.  Physics as a profession is separate and distinct from mathematics, although the two are sometimes confused.  Physics uses mathematics as its language and as its toolbox, but physics is not mathematics.  Physics is done best when it is done qualitatively—this means with scribbles done on napkins in restaurants or on the back of envelopes while waiting in line. Physics is about recognizing relationships and patterns. Physics is about identifying the limits to scaling properties where the physics changes when scales change. Physics is about the mapping of the simplest possible mathematics onto behavior in the physical world, and recognizing when the simplest possible mathematics is a universal that applies broadly to diverse systems that seem different, but that share the same underlying principles.

So, granted solving ODE’s is not physics, there is still a tremendous amount of good physics that can be done by solving ODE’s. ODE solvers become the modern physicist’s experimental workbench, providing data output from numerical experiments that can test the dependence on parameters in ways that real-world experiments might not be able to access. Physical intuition can be built based on such simulations as the engaged physicist begins to “understand” how the system behaves, able to explain what will happen as the values of parameters are changed.

In the follow sections, three examples of modern dynamics are introduced with a preliminary study, including Python code. These examples are: Galactic dynamics, synchronized networks and ecosystems. Despite their very different natures, their description using dynamical flows share features in common and illustrate the beauty and depth of behavior that can be explored with simple equations.

Galactic Dynamics

One example of the power and beauty of the vector flow equation and its set of all solutions in phase space is called the Henon-Heiles model of the motion of a star within a galaxy.  Of course, this is a terribly complicated problem that involves tens of billions of stars, but if you average over the gravitational potential of all the other stars, and throw in a couple of conservation laws, the resulting potential can look surprisingly simple.  The motion in the plane of this galactic potential takes two configuration coordinates (x, y) with two associated momenta (px, py) for a total of four dimensions.  The flow equations in four-dimensional phase space are simply

Fig. 1 The 4-dimensional phase space flow equations of a star in a galaxy. The terms in light blue are a simple two-dimensional harmonic oscillator. The terms in magenta are the nonlinear contributions from the stars in the galaxy.

where the terms in the light blue box describe a two-dimensional simple harmonic oscillator (SHO), which is a linear oscillator, modified by the terms in the magenta box that represent the nonlinear galactic potential.  The orbits of this Hamiltonian system are chaotic, and because there is no dissipation in the model, a single orbit will continue forever within certain ranges of phase space governed by energy conservation, but never quite repeating.

Fig. 2 Two-dimensional Poincaré section of sets of trajectories in four-dimensional phase space for the Henon-Heiles galactic dynamics model. The perturbation parameter is &eps; = 0.3411 and the energy E = 1.


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Wed Apr 18 06:03:32 2018

@author: nolte

Derived from:
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)

import numpy as np
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from scipy import integrate
from matplotlib import pyplot as plt
from matplotlib import cm
import time
import os


# model_case 1 = Heiles
# model_case 2 = Crescent
print(' ')
print('Case: 1 = Heiles')
print('Case: 2 = Crescent')
model_case = int(input('Enter the Model Case (1-2)'))

if model_case == 1:
    E = 1       # Heiles: 1, 0.3411   Crescent: 0.05, 1
    epsE = 0.3411   # 3411
    def flow_deriv(x_y_z_w,tspan):
        x, y, z, w = x_y_z_w
        a = z
        b = w
        c = -x - epsE*(2*x*y)
        d = -y - epsE*(x**2 - y**2)
    E = .1       #   Crescent: 0.1, 1
    epsE = 1   
    def flow_deriv(x_y_z_w,tspan):
        x, y, z, w = x_y_z_w
        a = z
        b = w
        c = -(epsE*(y-2*x**2)*(-4*x) + x)
        d = -(y-epsE*2*x**2)
prms = np.sqrt(E)
pmax = np.sqrt(2*E)    
# Potential Function
if model_case == 1:
    V = np.zeros(shape=(100,100))
    for xloop in range(100):
        x = -2 + 4*xloop/100
        for yloop in range(100):
            y = -2 + 4*yloop/100
            V[yloop,xloop] = 0.5*x**2 + 0.5*y**2 + epsE*(x**2*y - 0.33333*y**3) 
    V = np.zeros(shape=(100,100))
    for xloop in range(100):
        x = -2 + 4*xloop/100
        for yloop in range(100):
            y = -2 + 4*yloop/100
            V[yloop,xloop] = 0.5*x**2 + 0.5*y**2 + epsE*(2*x**4 - 2*x**2*y) 

fig = plt.figure(1)
contr = plt.contourf(V,100, cmap=cm.coolwarm, vmin = 0, vmax = 10)
fig.colorbar(contr, shrink=0.5, aspect=5)    
fig = plt.show()

repnum = 250
mulnum = 64/repnum

for reploop  in range(repnum):
    px1 = 2*(np.random.random((1))-0.499)*pmax
    py1 = np.sign(np.random.random((1))-0.499)*np.real(np.sqrt(2*(E-px1**2/2)))
    xp1 = 0
    yp1 = 0
    x_y_z_w0 = [xp1, yp1, px1, py1]
    tspan = np.linspace(1,1000,10000)
    x_t = integrate.odeint(flow_deriv, x_y_z_w0, tspan)
    siztmp = np.shape(x_t)
    siz = siztmp[0]

    if reploop % 50 == 0:
        lines = plt.plot(x_t[:,0],x_t[:,1])
        plt.setp(lines, linewidth=0.5)

    y1 = x_t[:,0]
    y2 = x_t[:,1]
    y3 = x_t[:,2]
    y4 = x_t[:,3]
    py = np.zeros(shape=(2*repnum,))
    yvar = np.zeros(shape=(2*repnum,))
    cnt = -1
    last = y1[1]
    for loop in range(2,siz):
        if (last < 0)and(y1[loop] > 0):
            cnt = cnt+1
            del1 = -y1[loop-1]/(y1[loop] - y1[loop-1])
            py[cnt] = y4[loop-1] + del1*(y4[loop]-y4[loop-1])
            yvar[cnt] = y2[loop-1] + del1*(y2[loop]-y2[loop-1])
            last = y1[loop]
            last = y1[loop]
    lines = plt.plot(yvar,py,'o',ms=1)
if model_case == 1:

Networks, Synchronization and Emergence

A central paradigm of nonlinear science is the emergence of patterns and organized behavior from seemingly random interactions among underlying constituents.  Emergent phenomena are among the most awe inspiring topics in science.  Crystals are emergent, forming slowly from solutions of reagents.  Life is emergent, arising out of the chaotic soup of organic molecules on Earth (or on some distant planet).  Intelligence is emergent, and so is consciousness, arising from the interactions among billions of neurons.  Ecosystems are emergent, based on competition and symbiosis among species.  Economies are emergent, based on the transfer of goods and money spanning scales from the local bodega to the global economy.

One of the common underlying properties of emergence is the existence of networks of interactions.  Networks and network science are topics of great current interest driven by the rise of the World Wide Web and social networks.  But networks are ubiquitous and have long been the topic of research into complex and nonlinear systems.  Networks provide a scaffold for understanding many of the emergent systems.  It allows one to think of isolated elements, like molecules or neurons, that interact with many others, like the neighbors in a crystal or distant synaptic connections.

From the point of view of modern dynamics, the state of a node can be a variable or a “dimension” and the interactions among links define the functions of the vector flow equation.  Emergence is then something that “emerges” from the dynamical flow as many elements interact through complex networks to produce simple or emergent patterns.

Synchronization is a form of emergence that happens when lots of independent oscillators, each vibrating at their own personal frequency, are coupled together to push and pull on each other, entraining all the individual frequencies into one common global oscillation of the entire system.  Synchronization plays an important role in the solar system, explaining why the Moon always shows one face to the Earth, why Saturn’s rings have gaps, and why asteroids are mainly kept away from colliding with the Earth.  Synchronization plays an even more important function in biology where it coordinates the beating of the heart and the functioning of the brain.

One of the most dramatic examples of synchronization is the Kuramoto synchronization phase transition. This occurs when a large set of individual oscillators with differing natural frequencies interact with each other through a weak nonlinear coupling.  For small coupling, all the individual nodes oscillate at their own frequency.  But as the coupling increases, there is a sudden coalescence of all the frequencies into a single common frequency.  This mechanical phase transition, called the Kuramoto transition, has many of the properties of a thermodynamic phase transition, including a solution that utilizes mean field theory.

Fig. 3 The Kuramoto model for the nonlinear coupling of N simple phase oscillators. The term in light blue is the simple phase oscillator. The term in magenta is the global nonlinear coupling that connects each oscillator to every other.

The simulation of 20 Poncaré phase oscillators with global coupling is shown in Fig. 4 as a function of increasing coupling coefficient g. The original individual frequencies are spread randomly. The oscillators with similar frequencies are the first to synchronize, forming small clumps that then synchronize with other clumps of oscillators, until all oscillators are entrained to a single compromise frequency. The Kuramoto phase transition is not sharp in this case because the value of N = 20 is too small. If the simulation is run for 200 oscillators, there is a sudden transition from unsynchronized to synchronized oscillation at a threshold value of g.

Fig. 4 The Kuramoto model for 20 Poincare oscillators showing the frequencies as a function of the coupling coefficient.

The Kuramoto phase transition is one of the most important fundamental examples of modern dynamics because it illustrates many facets of nonlinear dynamics in a very simple way. It highlights the importance of nonlinearity, the simplification of phase oscillators, the use of mean field theory, the underlying structure of the network, and the example of a mechanical analog to a thermodynamic phase transition. It also has analytical solutions because of its simplicity, while still capturing the intrinsic complexity of nonlinear systems.


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Sat May 11 08:56:41 2019

@author: nolte

Derived from:
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)

# https://www.python-course.eu/networkx.php
# https://networkx.github.io/documentation/stable/tutorial.html
# https://networkx.github.io/documentation/stable/reference/functions.html

import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
import networkx as nx
from UserFunction import linfit
import time

tstart = time.time()


Nfac = 20   # 25
N = 20      # 50
width = 0.2

# function: omegout, yout = coupleN(G)
def coupleN(G):

    # function: yd = flow_deriv(x_y)
    def flow_deriv(y,t0):
        yp = np.zeros(shape=(N,))
        for omloop  in range(N):
            temp = omega[omloop]
            linksz = G.node[omloop]['numlink']
            for cloop in range(linksz):
                cindex = G.node[omloop]['link'][cloop]
                g = G.node[omloop]['coupling'][cloop]

                temp = temp + g*np.sin(y[cindex]-y[omloop])
            yp[omloop] = temp
        yd = np.zeros(shape=(N,))
        for omloop in range(N):
            yd[omloop] = yp[omloop]
        return yd
    # end of function flow_deriv(x_y)

    mnomega = 1.0
    for nodeloop in range(N):
        omega[nodeloop] = G.node[nodeloop]['element']
    x_y_z = omega    
    # Settle-down Solve for the trajectories
    tsettle = 100
    t = np.linspace(0, tsettle, tsettle)
    x_t = integrate.odeint(flow_deriv, x_y_z, t)
    x0 = x_t[tsettle-1,0:N]
    t = np.linspace(1,1000,1000)
    y = integrate.odeint(flow_deriv, x0, t)
    siztmp = np.shape(y)
    sy = siztmp[0]
    # Fit the frequency
    m = np.zeros(shape = (N,))
    w = np.zeros(shape = (N,))
    mtmp = np.zeros(shape=(4,))
    btmp = np.zeros(shape=(4,))
    for omloop in range(N):
        if np.remainder(sy,4) == 0:
            mtmp[0],btmp[0] = linfit(t[0:sy//2],y[0:sy//2,omloop]);
            mtmp[1],btmp[1] = linfit(t[sy//2+1:sy],y[sy//2+1:sy,omloop]);
            mtmp[2],btmp[2] = linfit(t[sy//4+1:3*sy//4],y[sy//4+1:3*sy//4,omloop]);
            mtmp[3],btmp[3] = linfit(t,y[:,omloop]);
            sytmp = 4*np.floor(sy/4);
            mtmp[0],btmp[0] = linfit(t[0:sytmp//2],y[0:sytmp//2,omloop]);
            mtmp[1],btmp[1] = linfit(t[sytmp//2+1:sytmp],y[sytmp//2+1:sytmp,omloop]);
            mtmp[2],btmp[2] = linfit(t[sytmp//4+1:3*sytmp/4],y[sytmp//4+1:3*sytmp//4,omloop]);
            mtmp[3],btmp[3] = linfit(t[0:sytmp],y[0:sytmp,omloop]);

        #m[omloop] = np.median(mtmp)
        m[omloop] = np.mean(mtmp)
        w[omloop] = mnomega + m[omloop]
    omegout = m
    yout = y
    return omegout, yout
    # end of function: omegout, yout = coupleN(G)

Nlink = N*(N-1)//2      
omega = np.zeros(shape=(N,))
omegatemp = width*(np.random.rand(N)-1)
meanomega = np.mean(omegatemp)
omega = omegatemp - meanomega
sto = np.std(omega)

nodecouple = nx.complete_graph(N)

lnk = np.zeros(shape = (N,), dtype=int)
for loop in range(N):
    nodecouple.node[loop]['element'] = omega[loop]
    nodecouple.node[loop]['link'] = list(nx.neighbors(nodecouple,loop))
    nodecouple.node[loop]['numlink'] = np.size(list(nx.neighbors(nodecouple,loop)))
    lnk[loop] = np.size(list(nx.neighbors(nodecouple,loop)))

avgdegree = np.mean(lnk)
mnomega = 1

facval = np.zeros(shape = (Nfac,))
yy = np.zeros(shape=(Nfac,N))
xx = np.zeros(shape=(Nfac,))
for facloop in range(Nfac):
    facoef = 0.2

    fac = facoef*(16*facloop/(Nfac))*(1/(N-1))*sto/mnomega
    for nodeloop in range(N):
        nodecouple.node[nodeloop]['coupling'] = np.zeros(shape=(lnk[nodeloop],))
        for linkloop in range (lnk[nodeloop]):
            nodecouple.node[nodeloop]['coupling'][linkloop] = fac

    facval[facloop] = fac*avgdegree
    omegout, yout = coupleN(nodecouple)                           # Here is the subfunction call for the flow

    for omloop in range(N):
        yy[facloop,omloop] = omegout[omloop]

    xx[facloop] = facval[facloop]

lines = plt.plot(xx,yy)
plt.setp(lines, linewidth=0.5)

elapsed_time = time.time() - tstart
print('elapsed time = ',format(elapsed_time,'.2f'),'secs')

The Web of Life

Ecosystems are among the most complex systems on Earth.  The complex interactions among hundreds or thousands of species may lead to steady homeostasis in some cases, to growth and collapse in other cases, and to oscillations or chaos in yet others.  But the definition of species can be broad and abstract, referring to businesses and markets in economic ecosystems, or to cliches and acquaintances in social ecosystems, among many other examples.  These systems are governed by the laws of evolutionary dynamics that include fitness and survival as well as adaptation.

The dimensionality of the dynamical spaces for these systems extends to hundreds or thousands of dimensions—far too complex to visualize when thinking in four dimensions is already challenging.  Yet there are shared principles and common behaviors that emerge even here.  Many of these can be illustrated in a simple three-dimensional system that is represented by a triangular simplex that can be easily visualized, and then generalized back to ultra-high dimensions once they are understood.

A simplex is a closed (N-1)-dimensional geometric figure that describes a zero-sum game (game theory is an integral part of evolutionary dynamics) among N competing species.  For instance, a two-simplex is a triangle that captures the dynamics among three species.  Each vertex of the triangle represents the situation when the entire ecosystem is composed of a single species.  Anywhere inside the triangle represents the situation when all three species are present and interacting.

A classic model of interacting species is the replicator equation. It allows for a fitness-based proliferation and for trade-offs among the individual species. The replicator dynamics equations are shown in Fig. 5.

Fig. 5 Replicator dynamics has a surprisingly simple form, but with surprisingly complicated behavior. The key elements are the fitness and the payoff matrix. The fitness relates to how likely the species will survive. The payoff matrix describes how one species gains at the loss of another (although symbiotic relationships also occur).

The population dynamics on the 2D simplex are shown in Fig. 6 for several different pay-off matrices. The matrix values are shown in color and help interpret the trajectories. For instance the simplex on the upper-right shows a fixed point center. This reflects the antisymmetric character of the pay-off matrix around the diagonal. The stable spiral on the lower-left has a nearly asymmetric pay-off matrix, but with unequal off-diagonal magnitudes. The other two cases show central saddle points with stable fixed points on the boundary. A very large variety of behaviors are possible for this very simple system. The Python program is shown in Trirep.py.

Fig. 6 Payoff matrix and population simplex for four random cases: Upper left is an unstable saddle. Upper right is a center. Lower left is a stable spiral. Lower right is a marginal case.


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Thu May  9 16:23:30 2019

@author: nolte

Derived from:
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)

import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt


def tripartite(x,y,z):

    sm = x + y + z
    xp = x/sm
    yp = y/sm
    f = np.sqrt(3)/2
    y0 = f*xp
    x0 = -0.5*xp - yp + 1;
    lines = plt.plot(x0,y0)
    plt.setp(lines, linewidth=0.5)
    plt.plot([0, 1],[0, 0],'k',linewidth=1)
    plt.plot([0, 0.5],[0, f],'k',linewidth=1)
    plt.plot([1, 0.5],[0, f],'k',linewidth=1)

def solve_flow(y,tspan):
    def flow_deriv(y, t0):
    #"""Compute the time-derivative ."""
        f = np.zeros(shape=(N,))
        for iloop in range(N):
            ftemp = 0
            for jloop in range(N):
                ftemp = ftemp + A[iloop,jloop]*y[jloop]
            f[iloop] = ftemp
        phitemp = phi0          # Can adjust this from 0 to 1 to stabilize (but Nth population is no longer independent)
        for loop in range(N):
            phitemp = phitemp + f[loop]*y[loop]
        phi = phitemp
        yd = np.zeros(shape=(N,))
        for loop in range(N-1):
            yd[loop] = y[loop]*(f[loop] - phi);
        if np.abs(phi0) < 0.01:             # average fitness maintained at zero
            yd[N-1] = y[N-1]*(f[N-1]-phi);
        else:                                     # non-zero average fitness
            ydtemp = 0
            for loop in range(N-1):
                ydtemp = ydtemp - yd[loop]
            yd[N-1] = ydtemp
        return yd

    # Solve for the trajectories
    t = np.linspace(0, tspan, 701)
    x_t = integrate.odeint(flow_deriv,y,t)
    return t, x_t

# model_case 1 = zero diagonal
# model_case 2 = zero trace
# model_case 3 = asymmetric (zero trace)
print(' ')
print('Case: 1 = antisymm zero diagonal')
print('Case: 2 = antisymm zero trace')
print('Case: 3 = random')
model_case = int(input('Enter the Model Case (1-3)'))

N = 3
asymm = 3      # 1 = zero diag (replicator eqn)   2 = zero trace (autocatylitic model)  3 = random (but zero trace)
phi0 = 0.001            # average fitness (positive number) damps oscillations
T = 100;

if model_case == 1:
    Atemp = np.zeros(shape=(N,N))
    for yloop in range(N):
        for xloop in range(yloop+1,N):
            Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))
            Atemp[xloop,yloop] = -Atemp[yloop,xloop]

if model_case == 2:
    Atemp = np.zeros(shape=(N,N))
    for yloop in range(N):
        for xloop in range(yloop+1,N):
            Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))
            Atemp[xloop,yloop] = -Atemp[yloop,xloop]
        Atemp[yloop,yloop] = 2*(0.5 - np.random.random(1))
    tr = np.trace(Atemp)
    A = Atemp
    for yloop in range(N):
        A[yloop,yloop] = Atemp[yloop,yloop] - tr/N
    Atemp = np.zeros(shape=(N,N))
    for yloop in range(N):
        for xloop in range(N):
            Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))
    tr = np.trace(Atemp)
    A = Atemp
    for yloop in range(N):
        A[yloop,yloop] = Atemp[yloop,yloop] - tr/N

im = plt.matshow(A,3,cmap=plt.cm.get_cmap('seismic'))  # hsv, seismic, bwr
cbar = im.figure.colorbar(im)

M = 20
delt = 1/M
ep = 0.01;

tempx = np.zeros(shape = (3,))
for xloop in range(M):
    tempx[0] = delt*(xloop)+ep;
    for yloop in range(M-xloop):
        tempx[1] = delt*yloop+ep
        tempx[2] = 1 - tempx[0] - tempx[1]
        x0 = tempx/np.sum(tempx);          # initial populations
        tspan = 70
        t, x_t = solve_flow(x0,tspan)
        y1 = x_t[:,0]
        y2 = x_t[:,1]
        y3 = x_t[:,2]
        lines = plt.plot(t,y1,t,y2,t,y3)
        plt.setp(lines, linewidth=0.5)
        plt.ylabel('X Position')


Topics in Modern Dynamics

These three examples are just the tip of the iceberg. The topics in modern dynamics are almost numberless. Any system that changes in time is a potential object of study in modern dynamics. Here is a list of a few topics that spring to mind.


D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd Ed. (Oxford University Press, 2019) (The physics and the derivations of the equations for the examples in this blog can be found here.)

D. D. Nolte, Galileo Unbound: A Path Across Life, the Universe and Everything (Oxford University Press, 2018) (The historical origins of the examples in this blog can be found here.)

How Number Theory Protects You from the Chaos of the Cosmos

We are exceedingly fortunate that the Earth lies in the Goldilocks zone.  This zone is the range of orbital radii of a planet around its sun for which water can exist in a liquid state.  Water is the universal solvent, and it may be a prerequisite for the evolution of life.  If we were too close to the sun, water would evaporate as steam.  And if we are too far, then it would be locked in perpetual ice.  As it is, the Earth has had wild swings in its surface temperature.  There was once a time, more than 650 million years ago, when the entire Earth’s surface froze over.  Fortunately, the liquid oceans remained liquid, and life that already existed on Earth was able to persist long enough to get to the Cambrian explosion.  Conversely, Venus may once have had liquid oceans and maybe even nascent life, but too much carbon dioxide turned the planet into an oven and boiled away its water (a fate that may await our own Earth if we aren’t careful).  What has saved us so far is the stability of our orbit, our steady distance from the Sun that keeps our water liquid and life flourishing.  Yet it did not have to be this way. 

The regions of regular motion associated with irrational numbers act as if they were a barrier, restricting the range of chaotic orbits and protecting other nearby orbits from the chaos.

Our solar system is a many-body problem.  It consists of three large gravitating bodies (Sun, Jupiter, Saturn) and several minor ones (such as Earth).   Jupiter does influence our orbit, and if it were only a few times more massive than it actually is, then our orbit would become chaotic, varying in distance from the sun in unpredictable ways.  And if Jupiter were only about 20 times bigger than is actually is, there is a possibility that it would perturb the Earth’s orbit so strongly that it could eject the Earth from the solar system entirely, sending us flying through interstellar space, where we would slowly cool until we became a permanent ice ball.  What can protect us from this terrifying fate?  What keeps our orbit stable despite the fact that we inhabit a many-body solar system?  The answer is number theory!

The Most Irrational Number

What is the most irrational number you can think of? 

Is it: pi = 3.1415926535897932384626433 ? 

Or Euler’s constant: e = 2.7182818284590452353602874 ?

How about: sqrt(3) = 1.73205080756887729352744634 ?

These are all perfectly good irrational numbers.  But how do you choose the “most irrational” number?  The answer is fairly simple.  The most irrational number is the one that is least well approximated by a ratio of integers.  For instance, it is possible to get close to pi through the ratio 22/7 = 3.1428 which differs from pi by only 4 parts in ten thousand.  Or Euler’s constant 87/32 = 2.7188 differs from e by only 2 parts in ten thousand.  Yet 87 and 32 are much bigger than 22 and 7, so it may be said that e is more irrational than pi, because it takes ratios of larger integers to get a good approximation.  So is there a “most irrational” number?  The answer is yes.  The Golden Ratio.

The Golden ratio can be defined in many ways, but its most common expression is given by

It is the hardest number to approximate with a ratio of small integers.  For instance, to get a number that is as close as one part in ten thousand to the golden mean takes the ratio 89/55.  This result may seem obscure, but there is a systematic way to find the ratios of integers that approximate an irrational number. This is known as a convergent from continued fractions.

Continued fractions were invented by John Wallis in 1695, introduced in his book Opera Mathematica.  The continued fraction for pi is

An alternate form of displaying this continued fraction is with the expression

The irrational character of pi is captured by the seemingly random integers in this string. However, there can be regular structure in irrational numbers. For instance, a different continued fraction for pi is

that has a surprisingly simple repeating pattern.

The continued fraction for the golden mean has an especially simple repeating form


This continued fraction has the slowest convergence for its continued fraction of any other number. Hence, the Golden Ratio can be considered, using this criterion, to be the most irrational number.

If the Golden Ratio is the most irrational number, how does that save us from the chaos of the cosmos? The answer to this question is KAM!

Kolmogorov, Arnold and Moser: (KAM) Theory

KAM is an acronym made from the first initials of three towering mathematicians of the 20th century: Andrey Kolmogorov (1903 – 1987), his student Vladimir Arnold (1937 – 2010), and Jürgen Moser (1928 – 1999).

In 1954, Kolmogorov, considered to be the greatest living mathematician at that time, was invited to give the plenary lecture at a mathematics conference. To the surprise of the conference organizers, he chose to talk on what seemed like a very mundane topic: the question of the stability of the solar system. This had been the topic which Poincaré had attempted to solve in 1890 when he first stumbled on chaotic dynamics. The question had remained open, but the general consensus was that the many-body nature of the solar system made it intrinsically unstable, even for only three bodies.

Against all expectations, Kolmogorov proposed that despite the general chaotic behavior of the three–body problem, there could be “islands of stability” which were protected from chaos, allowing some orbits to remain regular even while other nearby orbits were highly chaotic. He even outlined an approach to a proof of his conjecture, though he had not carried it through to completion.

The proof of Kolmogorov’s conjecture was supplied over the next 10 years through the work of the German mathematician Jürgen Moser and by Kolmogorov’s former student Vladimir Arnold. The proof hinged on the successive ratios of integers that approximate irrational numbers. With this work KAM showed that indeed some orbits are actually protected from neighboring chaos by relying on the irrationality of the ratio of orbital periods.

Resonant Ratios

Let’s go back to the simple model of our solar system that consists of only three bodies: the Sun, Jupiter and Earth. The period of Jupiter’s orbit is 11.86 years, but instead, if it were exactly 12 years, then its period would be in a 12:1 ratio with the Earth’s period. This ratio of integers is called a “resonance”, although in this case it is fairly mismatched. But if this ratio were a ratio of small integers like 5:3, then it means that Jupiter would travel around the sun 5 times in 15 years while the Earth went around 3 times. And every 15 years, the two planets would align. This kind of resonance with ratios of small integers creates a strong gravitational perturbation that alters the orbit of the smaller planet. If the perturbation is strong enough, it could disrupt the Earth’s orbit, creating a chaotic path that might ultimately eject the Earth completely from the solar system.

What KAM discovered is that as the resonance ratio becomes a ratio of large integers, like 87:32, then the planets have a hard time aligning, and the perturbation remains small. A surprising part of this theory is that a nearby orbital ratio might be 5:2 = 1.5, which is only a little different than 87:32 = 1.7. Yet the 5:2 resonance can produce strong chaos, while the 87:32 resonance is almost immune. This way, it is possible to have both chaotic orbits and regular orbits coexisting in the same dynamical system. An irrational orbital ratio protects the regular orbits from chaos. The next question is, how irrational does the orbital ratio need to be to guarantee safety?

You probably already guessed the answer to this question–the answer must be the Golden Ratio. If this is indeed the most irrational number, then it cannot be approximated very well with ratios of small integers, and this is indeed the case. In a three-body system, the most stable orbital ratio would be a ratio of 1.618034. But the more general question of what is “irrational enough” for an orbit to be stable against a given perturbation is much harder to answer. This is the field of Diophantine Analysis, which addresses other questions as well, such as Fermat’s Last Theorem.

KAM Twist Map

The dynamics of three-body systems are hard to visualize directly, so there are tricks that help bring the problem into perspective. The first trick, invented by Henri Poincaré, is called the first return map (or the Poincaré section). This is a way of reducing the dimensionality of the problem by one dimension. But for three bodies, even if they are all in a plane, this still can be complicated. Another trick, called the restricted three-body problem, is to assume that there are two large masses and a third small mass. This way, the dynamics of the two-body system is unaffected by the small mass, so all we need to do is focus on the dynamics of the small body. This brings the dynamics down to two dimensions (the position and momentum of the third body), which is very convenient for visualization, but the dynamics still need solutions to differential equations. So the final trick is to replace the differential equations with simple difference equations that are solved iteratively.

A simple discrete iterative map that captures the essential behavior of the three-body problem begins with action-angle variables that are coupled through a perturbation. Variations on this model have several names: the Twist Map, the Chirikov Map and the Standard Map. The essential mapping is

where J is an action variable (like angular momentum) paired with the angle variable. Initial conditions for the action and the angle are selected, and then all later values are obtained by iteration. The perturbation parameter is given by ε. If ε = 0 then all orbits are perfectly regular and circular. But as the perturbation increases, the open orbits split up into chains of closed (periodic) orbits. As the perturbation increases further, chaotic behavior emerges. The situation for ε = 0.9 is shown in the figure below. There are many regular periodic orbits as well as open orbits. Yet there are simultaneously regions of chaotic behavior. This figure shows an intermediate case where regular orbits can coexist with chaotic ones. The key is the orbital period ratio. For orbital ratios that are sufficiently irrational, the orbits remain open and regular. Bur for orbital ratios that are ratios of small integers, the perturbation is strong enough to drive the dynamics into chaos.

Arnold Twist Map (also known as a Chirikov map) for ε = 0.9 showing the chaos that has emerged at the hyperbolic point, but there are still open orbits that are surprisingly circular (unperturbed) despite the presence of strongly chaotic orbits nearby.

Python Code

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Wed Oct. 2, 2019
@author: nolte
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt

eps = 0.9

for eloop in range(0,50):

    rlast = np.pi*(1.5*np.random.random()-0.5)
    thlast = 2*np.pi*np.random.random()

    orbit = np.int(200*(rlast+np.pi/2))
    rplot = np.zeros(shape=(orbit,))
    thetaplot = np.zeros(shape=(orbit,))
    x = np.zeros(shape=(orbit,))
    y = np.zeros(shape=(orbit,))    
    for loop in range(0,orbit):
        rnew = rlast + eps*np.sin(thlast)
        thnew = np.mod(thlast+rnew,2*np.pi)
        rplot[loop] = rnew
        thetaplot[loop] = np.mod(thnew-np.pi,2*np.pi) - np.pi            
        rlast = rnew
        thlast = thnew
        x[loop] = (rnew+np.pi+0.25)*np.cos(thnew)
        y[loop] = (rnew+np.pi+0.25)*np.sin(thnew)


The twist map for three values of ε are shown in the figure below. For ε = 0.2, most orbits are open, with one elliptic point and its associated hyperbolic point. At ε = 0.9 the periodic elliptic point is still stable, but the hyperbolic point has generated a region of chaotic orbits. There is still a remnant open orbit that is associated with an orbital period ratio at the Golden Ratio. However, by ε = 0.97, even this most stable orbit has broken up into a chain of closed orbits as the chaotic regions expand.

Twist map for three levels of perturbation.

Safety in Numbers

In our solar system, governed by gravitational attractions, the square of the orbital period increases as the cube of the average radius (Kepler’s third law). Consider the restricted three-body problem of the Sun and Jupiter with the Earth as the third body. If we analyze the stability of the Earth’s orbit as a function of distance from the Sun, the orbital ratio relative to Jupiter would change smoothly. Near our current position, it would be in a 12:1 resonance, but as we moved farther from the Sun, this ratio would decrease. When the orbital period ratio is sufficiently irrational, then the orbit would be immune to Jupiter’s pull. But as the orbital ratio approaches ratios of integers, the effect gets larger. Close enough to Jupiter there would be a succession of radii that had regular motion separated by regions of chaotic motion. The regions of regular motion associated with irrational numbers act as if they were a barrier, restricting the range of chaotic orbits and protecting more distant orbits from the chaos. In this way numbers, rational versus irrational, protect us from the chaos of our own solar system.

A dramatic demonstration of the orbital resonance effect can be seen with the asteroid belt. The many small bodies act as probes of the orbital resonances. The repetitive tug of Jupiter opens gaps in the distribution of asteroid radii, with major gaps, called Kirkwood Gaps, opening at orbital ratios of 3:1, 5:2, 7:3 and 2:1. These gaps are the radii where chaotic behavior occurs, while the regions in between are stable. Most asteroids spend most of their time in the stable regions, because chaotic motion tends to sweep them out of the regions of resonance. This mechanism for the Kirkwood gaps is the same physics that produces gaps in the rings of Saturn at resonances with the many moons of Saturn.

The gaps in the asteroid distributions caused by orbital resonances with Jupiter. Ref. Wikipedia

Further Reading

For a detailed history of the development of KAM theory, see Chapter 9 Butterflies to Hurricanes in Galileo Unbound (Oxford University Press, 2018).

For a more detailed mathematical description of the KAM theory, see Chapter 5, Hamiltonian Chaos, in Introduction to Modern Dynamics, 2nd edition (Oxford University Press, 2019).

See also:

Dumas, H. S., The KAM Story: A friendly introduction to the content, history and significance of Classical Kolmogorov-Arnold-Moser Theory. World Scientific: 2014.

Arnold, V. I., From superpositions to KAM theory. Vladimir Igorevich Arnold. Selected Papers 1997, PHASIS, 60, 727–740.

Vladimir Arnold’s Cat Map

The 1960’s are known as a time of cultural revolution, but perhaps less known was the revolution that occurred in the science of dynamics.  Three towering figures of that revolution were Stephen Smale (1930 – ) at Berkeley, Andrey Kolmogorov (1903 – 1987) in Moscow and his student Vladimir Arnold (1937 – 2010).  Arnold was only 20 years old in 1957 when he solved Hilbert’s thirteenth problem (that any continuous function of several variables can be constructed with a finite number of two-variable functions).  Only a few years later his work on the problem of small denominators in dynamical systems provided the finishing touches on the long elusive explanation of the stability of the solar system (the problem for which Poincaré won the King Oscar Prize in mathematics in 1889 when he discovered chaotic dynamics ).  This theory is known as KAM-theory, using the first initials of the names of Kolmogorov, Arnold and Moser [1].  Building on his breakthrough in celestial mechanics, Arnold’s work through the 1960’s remade the theory of Hamiltonian systems, creating a shift in perspective that has permanently altered how physicists look at dynamical systems.

Hamiltonian Physics on a Torus

Traditionally, Hamiltonian physics is associated with systems of inertial objects that conserve the sum of kinetic and potential energy, in other words, conservative non-dissipative systems.  But a modern view (after Arnold) of Hamiltonian systems sees them as hyperdimensional mathematical mappings that conserve volume.  The space that these mappings inhabit is phase space, and the conservation of phase-space volume is known as Liouville’s Theorem [2].  The geometry of phase space is called symplectic geometry, and the universal position that symplectic geometry now holds in the physics of Hamiltonian mechanics is largely due to Arnold’s textbook Mathematical Methods of Classical Mechanics (1974, English translation 1978) [3]. Arnold’s famous quote from that text is “Hamiltonian mechanics is geometry in phase space”. 

One of the striking aspects of this textbook is the reduction of phase-space geometry to the geometry of a hyperdimensional torus for a large number of Hamiltonian systems.  If there are as many conserved quantities as there are degrees of freedom in a Hamiltonian system, then the system is called “integrable” (because you can integrated the equations of motion to find a constant of the motion). Then it is possible to map the physics onto a hyperdimensional torus through the transformation of dynamical coordinates into what are known as “action-angle” coordinates [4].  Each independent angle has an associated action that is conserved during the motion of the system.  The periodicity of the dynamical angle coordinate makes it possible to identify it with the angular coordinate of a multi-dimensional torus.  Therefore, every integrable Hamiltonian system can be mapped to motion on a multi-dimensional torus (one dimension for each degree of freedom of the system). 

Actually, integrable Hamiltonian systems are among the most boring dynamical systems you can imagine. They literally just go in circles (around the torus). But as soon as you add a small perturbation that cannot be integrated they produce some of the most complex and beautiful patterns of all dynamical systems. It was Arnold’s focus on motions on a torus, and perturbations that shift the dynamics off the torus, that led him to propose a simple mapping that captured the essence of Hamiltonian chaos.

The Arnold Cat Map

Motion on a two-dimensional torus is defined by two angles, and trajectories on a two-dimensional torus are simple helixes. If the periodicities of the motion in the two angles have an integer ratio, the helix repeats itself. However, if the ratio of periods (also known as the winding number) is irrational, then the helix never repeats and passes arbitrarily closely to any point on the surface of the torus. This last case leads to an “ergodic” system, which is a term introduced by Boltzmann to describe a physical system whose trajectory fills phase space. The behavior of a helix for rational or irrational winding number is not terribly interesting. It’s just an orbit going in circles like an integrable Hamiltonian system. The helix can never even cross itself.

However, if you could add a new dimension to the torus (or add a new degree of freedom to the dynamical system), then the helix could pass over or under itself by moving into the new dimension. By weaving around itself, a trajectory can become chaotic, and the set of many trajectories can become as mixed up as a bowl of spaghetti. This can be a little hard to visualize, especially in higher dimensions, but Arnold thought of a very simple mathematical mapping that captures the essential motion on a torus, preserving volume as required for a Hamiltonian system, but with the ability for regions to become all mixed up, just like trajectories in a nonintegrable Hamiltonian system.

A unit square is isomorphic to a two-dimensional torus. This means that there is a one-to-one mapping of each point on the unit square to each point on the surface of a torus. Imagine taking a sheet of paper and forming a tube out of it. One of the dimensions of the sheet of paper is now an angle coordinate that is cyclic, going around the circumference of the tube. Now if the sheet of paper is flexible (like it is made of thin rubber) you can bend the tube around and connect the top of the tube with the bottom, like a bicycle inner tube. The other dimension of the sheet of paper is now also an angle coordinate that is cyclic. In this way a flat sheet is converted (with some bending) into a torus.

Arnold’s key idea was to create a transformation that takes the torus into itself, preserving volume, yet including the ability for regions to pass around each other. Arnold accomplished this with the simple map

where the modulus 1 takes the unit square into itself. This transformation can also be expressed as a matrix

followed by taking modulus 1. The transformation matrix is called a Floquet matrix, and the determinant of the matrix is equal to unity, which ensures that volume is conserved.

Arnold decided to illustrate this mapping by using a crude image of the face of a cat (See Fig. 1). Successive applications of the transformation stretch and shear the cat, which is then folded back into the unit square. The stretching and folding preserve the volume, but the image becomes all mixed up, just like mixing in a chaotic Hamiltonian system, or like an immiscible dye in water that is stirred.

Fig. 1 Arnold’s illustration of his cat map from pg. 6 of V. I. Arnold and A. Avez, Ergodic Problems of Classical Mechanics (Benjamin, 1968) [5]
Fig. 2 Arnold Cat Map operation is an iterated succession of stretching with shear of a unit square, and translation back to the unit square. The mapping preserves and mixes areas, and is invertible.


When the transformation matrix is applied to continuous values, it produces a continuous range of transformed values that become thinner and thinner until the unit square is uniformly mixed. However, if the unit square is discrete, made up of pixels, then something very different happens (see Fig. 3). The image of the cat in this case is composed of a 50×50 array of pixels. For early iterations, the image becomes stretched and mixed, but at iteration 50 there are 4 low-resolution upside-down versions of the cat, and at iteration 75 the cat fully reforms, but is upside-down. Continuing on, the cat eventually reappears fully reformed and upright at iteration 150. Therefore, the discrete case displays a recurrence and the mapping is periodic. Calculating the period of the cat map on lattices can lead to interesting patterns, especially if the lattice is composed of prime numbers [6].

Fig. 3 A discrete cat map has a recurrence period. This example with a 50×50 lattice has a period of 150.

The Cat Map and the Golden Mean

The golden mean, or the golden ratio, 1.618033988749895 is never far away when working with Hamiltonian systems. Because the golden mean is the “most irrational” of all irrational numbers, it plays an essential role in KAM theory on the stability of the solar system. In the case of Arnold’s cat map, it pops up its head in several ways. For instance, the transformation matrix has eigenvalues

with the remarkable property that

which guarantees conservation of area.

Selected V. I. Arnold Publications

Arnold, V. I. “FUNCTIONS OF 3 VARIABLES.” Doklady Akademii Nauk Sssr 114(4): 679-681. (1957)

Arnold, V. I. “GENERATION OF QUASI-PERIODIC MOTION FROM A FAMILY OF PERIODIC MOTIONS.” Doklady Akademii Nauk Sssr 138(1): 13-&. (1961)





Arnold, V. I. and Y. G. Sinai. “SMALL PERTURBATIONS OF AUTHOMORPHISMS OF A TORE.” Doklady Akademii Nauk Sssr 144(4): 695-&. (1962)

Arnold, V. I. “Small denominators and problems of the stability of motion in classical and celestial mechanics (in Russian).” Usp. Mat. Nauk. 18: 91-192. (1963)


Arnold, V. I. “INSTABILITY OF DYNAMICAL SYSTEMS WITH MANY DEGREES OF FREEDOM.” Doklady Akademii Nauk Sssr 156(1): 9-&. (1964)

Arnold, V. “SUR UNE PROPRIETE TOPOLOGIQUE DES APPLICATIONS GLOBALEMENT CANONIQUES DE LA MECANIQUE CLASSIQUE.” Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences 261(19): 3719-&. (1965)



[1] Dumas, H. S. The KAM Story: A friendly introduction to the content, history and significance of Classical Kolmogorov-Arnold-Moser Theory, World Scientific. (2014)

[2] See Chapter 6, “The Tangled Tale of Phase Space” in Galileo Unbound (D. D. Nolte, Oxford University Press, 2018)

[3] V. I. Arnold, Mathematical Methods of Classical Mechanics (Nauk 1974, English translation Springer 1978)

[4] See Chapter 3, “Hamiltonian Dynamics and Phase Space” in Introduction to Modern Dynamics, 2nd ed. (D. D. Nolte, Oxford University Press, 2019)

[5] V. I. Arnold and A. Avez, Ergodic Problems of Classical Mechanics (Benjamin, 1968)

[6] Gaspari, G. “THE ARNOLD CAT MAP ON PRIME LATTICES.” Physica D-Nonlinear Phenomena 73(4): 352-372. (1994)

Top 10 Topics of Modern Dynamics

“Modern physics” in the undergraduate physics curriculum has been monopolized, on the one hand, by quantum mechanics, nuclear physics, particle physics and astrophysics. “Classical mechanics”, on the other hand, has been monopolized by Lagrangians and Hamiltonians.  While these are all admittedly interesting, the topics of modern dynamics that monopolize the time and actions of most physics-degree holders, as they work in high-tech start-ups, established technology companies, or on Wall Street, are not to be found.  These are the topics of nonlinear dynamics, chaos theory, complex networks, finance, evolutionary dynamics and neural networks, among others.


There is a growing awareness that the undergraduate physics curriculum needs to be reinvigorated to make a physics degree relevant to the modern workplace.  To that end, I am listing my top 10 topics of modern dynamics that can form the foundation of a revamped upper-division (junior level) mechanics course.  Virtually all of these topics were once reserved for graduate-student-level courses, but all can be introduced to undergraduates in simple and intuitive ways without the need for advanced math.

1) Phase Space

The key change in perspective for modern dynamics that differentiates it from classical dynamics is the emphasis on the set of all possible trajectories that fill a “space” rather than emphasizing single trajectories defined by given initial conditions.  Rather than study the motion of one rock thrown from a cliff top, modern dynamics studies an infinity of rocks thrown from every possible point and with every possible velocity.  The space that contains this infinity of trajectories is known as phase space (or more generally state space).  The equation of motion in state space becomes the dynamical flow, replacing Newton’s second law as the central mathematical structure of physics.  Modern dynamics studies the properties of phase space rather than the properties of single trajectories, and makes rigorous and unique conclusions about classes of possible motions.  This emphasis on classes of behavior is more general and more universal and more powerful, while also providing a fundamental “visual language” with which to describe the complex physics of complex systems.

2) Metric Space

The Cartesian coordinate plane that we were all taught in high school tends to dominate our thinking, biasing us towards linear flat geometries.  Yet most dynamics do not take place in such simple Cartesian spaces.  A case in point, virtually every real-world dynamics problem has constraints that confine the motion to a surface.  Furthermore, the number of degrees of freedom of a dynamical system usually exceed our common 3-space, expanding to hundreds or even to thousands of dimensions.  The surfaces of constraint are hypersurfaces of high dimensions (known as manifolds) and are almost certainly not flat hyperplanes. This daunting prospect of high-dimensional warped spaces can be surprisingly simplified through the concept of Bernhard Riemann’s “metric space”.  Understanding the geometry of a metric space can be as simple as applying Pythagoras’ Theorem to sets of coordinates.  For instance, the metric tensor can be taught and used without requiring students to know anything of tensor calculus.  At the same time, it provides a useful tool for understanding dynamical patterns in phase space as well as orbits around black holes.

3) Invariants

Introductory physics classes emphasize the conservation of energy, linear momentum and angular momentum as if they are special cases.  Yet there is a grand structure that yields a universal set of conservation laws: integrable Hamiltonian systems.  An integrable system is one for which there are as many invariants of motion as there are degrees of freedom.  Amazingly, these conservation laws can all be captured by a single procedure known as (canonical) transformation to action-angle coordinates.  When expressed in action-angle form, these Hamiltonians take on extremely simple expressions.  They are also the starting point for the study of perturbations when small nonintegrable terms are added to the Hamiltonian.  As the perturbations grow, this provides one doorway to the emergence of chaos.

4) Chaos theory

“Chaos theory” is the more popular title for what is generally called “nonlinear dynamics”.  Nonlinear dynamics takes place in state space when the dynamical flow equations have terms that algebraically are products of variables.  One important distinction between chaos theory and nonlinear dynamics is the occurrence of unpredictability that can emerge in the dynamics when the number of variables is equal to three or higher.  The equations, and the resulting dynamics, are still deterministic, but the trajectories are incredibly sensitive to initial conditions (SIC).  In addition, the dynamical trajectories can relax to a submanifold of the original state space known as a strange attractor that typically is a fractal structure.

5) Synchronization

One of the central paradigms of nonlinear dynamics is the autonomous oscillator.  Unlike the harmonic oscillator that eventually decays due to friction, autonomous oscillators are steady-state oscillators that convert steady energy input into oscillatory behavior.  A prime example is the pendulum clock that converts the steady weight of a hanging mass into a sustained oscillation.  When two autonomous oscillators (that normally oscillator at slightly different frequencies) are coupled weakly together, they can synchronize to the same frequency.   This effect was discovered by Christiaan Huygens when he observed two pendulum clocks hanging next to each other on a wall synchronize the swings of their pendula.  Synchronization is a central paradigm in modern dynamics for several reasons.  First, it demonstrates the emergence of order when a collective behavior emerges from a collection of individual systems (this phenomenon of emergence is one of the fundamental principles of complex system science).  Second, synchronized systems include such critical systems as the beating heart and the thinking brain.  Third, synchronization becomes a useful tool to explore coupled systems that have a large number of linked subsystems, as in networks of nodes.

6) Network Dynamics

Networks have become one of the driving forces of our modern interconnected society.  The structure of networks, the dynamics of nodes in networks, and the dynamic growth of networks are all coming into focus as we live our lives in multiple interconnected webs.  Dynamics on networks include problems like diffusion and the spread of infection and connect with topics of percolation theory and critical phenomenon.  Nonlinear dynamics on networks provide key opportunities and examples to study complex interacting systems.

7) Neural Networks

Perhaps the most enigmatic network is the network of neurons in the brain.  The emergence of intelligence and of sentience is one of the greatest scientific questions.  At a much simpler level, the nonlinear dynamics of small numbers of neurons display the properties of autonomous oscillators and synchronization, while larger sets of neurons become interconnected into dynamic networks.  The dynamics of neurons and of neural networks is a  key topic in modern dynamics.  Not only can the physics of the networks be studied, but neural networks become tools for studying other complex systems.

8) Evolutionary Dynamics

The emergence of life and the evolution of species stands as another of the greatest scientific questions of our day.  Although this topic traditionally is studied by the biological sciences (and mathematical biology), physics has a surprising lot to say on the topic.  The dynamics of evolution can be captured in the same types of nonlinear flows that live in state space.  For instance, population dynamics can be described as a large ensemble of interacting individuals that are born, flourish and die dependent on their environment and on their complicated interactions with other members in their ecosystem.  These types of problems have state spaces of extremely high dimension far beyond what we can visualize.  Yet the emergence of structure and of patterns from the complex dynamics helps to reduce the complexity, as do conceptual metaphors like evolutionary fitness landscapes.

9) Economic Dynamics

A non-negligible fraction of both undergraduate and graduate physics degree holders end up on Wall Street or in related industries.  This is partly because physicists are numerically fluent while also possessing sound intuition.  Therefore, economic dynamics is a potentially valuable addition to the modern dynamics curriculum and easily expressed using the concepts of dynamical flows and state space.  Both microeconomics (business competition, business cycles) and macroeconomics (investment and savings, liquidity and money, inflation, unemployment) can be described and analyzed using mathematical flows that are the central toolkit of modern dynamics.

10) Relativity

Special relativity is a common topic in the current upper-division physics curriculum, while general relativity is viewed as too difficult to expose undergraduates to.  This is mostly an artificial division, because Einstein’s “happiest thought” occurred when he realized that an observer in free fall is in a force-free (inertial) frame.  The equivalence principle, that states that a frame in uniform acceleration is indistinguishable from a stationary frame in a uniform gravitational field, opens a wide door that connects special relativity to general relativity.  In an undergraduate course on modern dynamics, the metric tensor (described above) is introduced in simple terms, providing the foundation to develop Minkowski spacetime, and the next natural extension is to warped spacetime—all at the simple level of linear algebra combined with partial differentiation.  General relativity ties in many of the principles of the modern dynamics curriculum (dynamical flows, state space, metric space, invariants, nonlinear dynamics), and the students can simulate orbits around black holes with ease.  I have been teaching General Relativity to undergraduates for over ten years now, and it is a highlight of the course.

Introduction to Modern Dynamics

For further reading and more details, these top 10 topics of modern dynamics are defined and explored in the undergraduate physics textbook “Introduction to Modern Dynamics: Chaos, Networks, Space and Time” published by Oxford University Press (Second Edition: 2019).  This textbook is designed for use in a two-semester junior-level mechanics course.  It introduces the topics of modern dynamics, while still presenting traditional materials that the students need for their physics GREs.