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. Chaos: Classical and QuantumI: Deterministic Chaos
Predrag Cvitanovic Roberto Artuso Ronnie Mainieri Gregor Tanner
Gabor Vattay
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ChaosBook.org version15.6, Mar 15 2015 printed March 25,
2015ChaosBook.org comments to: [email protected]
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Contents
0.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . xvContents . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . xivAcknowledgments . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . xviii
I Geometry of chaos 1
1 Overture 31.1 Why ChaosBook? . . . . . . . . . . . . . . . . .
. . . . . . . . . 41.2 Chaos ahead . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 51.3 The future as in a mirror . . . . .
. . . . . . . . . . . . . . . . . 61.4 A game of pinball . . . . .
. . . . . . . . . . . . . . . . . . . . . 111.5 Chaos for cyclists
. . . . . . . . . . . . . . . . . . . . . . . . . . 151.6 Change in
time . . . . . . . . . . . . . . . . . . . . . . . . . . . 211.7 To
statistical mechanics . . . . . . . . . . . . . . . . . . . . . . .
241.8 Chaos: what is it good for? . . . . . . . . . . . . . . . . .
. . . . 251.9 What is not in ChaosBook . . . . . . . . . . . . . .
. . . . . . . 28resume 28 commentary 30 guide to exercises 33
exercises 34 references 34
2 Go with the flow 372.1 Dynamical systems . . . . . . . . . . .
. . . . . . . . . . . . . . 372.2 Flows . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 422.3 Changing coordinates . .
. . . . . . . . . . . . . . . . . . . . . . 462.4 Life in extreme
dimensions . . . . . . . . . . . . . . . . . . . . . 472.5
Computing trajectories . . . . . . . . . . . . . . . . . . . . . .
. 52resume 53commentary 542.6 Examples . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 57exercises 60 references 62
3 Discrete time dynamics 663.1 Poincare sections . . . . . . . .
. . . . . . . . . . . . . . . . . . 673.2 Computing a Poincare
section . . . . . . . . . . . . . . . . . . . 713.3 Mappings . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 73resume 75
commentary 763.4 Examples . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 78exercises 81 references 82
ii
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CONTENTS iii
4 Local stability 844.1 Flows transport neighborhoods . . . . .
. . . . . . . . . . . . . . 844.2 Computing the Jacobian matrix . .
. . . . . . . . . . . . . . . . . 884.3 A linear diversion . . . .
. . . . . . . . . . . . . . . . . . . . . . 894.4 Stability of
flows . . . . . . . . . . . . . . . . . . . . . . . . . . 904.5
Stability of maps . . . . . . . . . . . . . . . . . . . . . . . . .
. 914.6 Stability of Poincare return maps . . . . . . . . . . . . .
. . . . . 924.7 Neighborhood volume . . . . . . . . . . . . . . . .
. . . . . . . 94resume 95 commentary 964.8 Examples . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 97exercises 103
references 104
5 Cycle stability 1075.1 Equilibria . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 1085.2 Periodic orbits . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 1085.3 Floquet
multipliers are invariant . . . . . . . . . . . . . . . . . .
1125.4 Floquet multipliers are metric invariants . . . . . . . . .
. . . . . 1145.5 Stability of Poincare map cycles . . . . . . . . .
. . . . . . . . . 1155.6 There goes the neighborhood . . . . . . .
. . . . . . . . . . . . . 116resume 117 commentary 1175.7 Examples
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
118exercises 120 references 120
6 Lyapunov exponents 1216.1 Stretch, strain and twirl . . . . .
. . . . . . . . . . . . . . . . . . 1226.2 Lyapunov exponents . . .
. . . . . . . . . . . . . . . . . . . . . 123resume 126 commentary
1266.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 129exercises 129 references 130
7 Fixed points 1337.1 One-dimensional maps . . . . . . . . . . .
. . . . . . . . . . . . 1347.2 Flows . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 136resume 137 commentary 1387.3
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 138exercises 141 references 141
8 Hamiltonian dynamics 1448.1 Hamiltonian flows . . . . . . . .
. . . . . . . . . . . . . . . . . . 1458.2 Symplectic group . . . .
. . . . . . . . . . . . . . . . . . . . . . 1478.3 Stability of
Hamiltonian flows . . . . . . . . . . . . . . . . . . . 1498.4
Symplectic maps . . . . . . . . . . . . . . . . . . . . . . . . . .
1518.5 Poincare invariants . . . . . . . . . . . . . . . . . . . .
. . . . . 154resume 155 commentary 156 exercises 159 references
160
9 Billiards 1629.1 Billiard dynamics . . . . . . . . . . . . . .
. . . . . . . . . . . . 1629.2 Stability of billiards . . . . . . .
. . . . . . . . . . . . . . . . . . 164resume 167 commentary 167
exercises 168 references 169
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CONTENTS iv
10 Flips, slides and turns 17110.1 Discrete symmetries . . . . .
. . . . . . . . . . . . . . . . . . . . 17110.2 Subgroups, cosets,
classes . . . . . . . . . . . . . . . . . . . . . 17410.3 Orbits,
quotient space . . . . . . . . . . . . . . . . . . . . . . . .
176resume 178 commentary 17910.4 Examples . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 180exercises 182 references
182
11 World in a mirror 18411.1 Symmetries of solutions . . . . . .
. . . . . . . . . . . . . . . . 18411.2 Relative periodic orbits .
. . . . . . . . . . . . . . . . . . . . . . 18711.3 Dynamics
reduced to fundamental domain . . . . . . . . . . . . . 18911.4
Invariant polynomials . . . . . . . . . . . . . . . . . . . . . . .
. 190resume 191 commentary 19211.5 Examples . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 193exercises 197 references
198
12 Relativity for cyclists 20012.1 Continuous symmetries . . . .
. . . . . . . . . . . . . . . . . . . 20112.2 Symmetries of
solutions . . . . . . . . . . . . . . . . . . . . . . 20612.3
Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 211resume 212 commentary 21312.4 Examples . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 214exercises 219 references
219
13 Slice & dice 22213.1 Only dead fish go with the flow:
moving frames . . . . . . . . . . 22313.2 Symmetry reduction . . .
. . . . . . . . . . . . . . . . . . . . . . 22413.3 Bringing it all
back home: method of slices . . . . . . . . . . . . 22513.4
Dynamics within a slice . . . . . . . . . . . . . . . . . . . . . .
. 22713.5 First Fourier mode slice . . . . . . . . . . . . . . . .
. . . . . . . 22913.6 Stability within a slice . . . . . . . . . .
. . . . . . . . . . . . . 23113.7 Method of images: Hilbert bases .
. . . . . . . . . . . . . . . . . 233resume 234 commentary 23513.8
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 240exercises 240 references 241
14 Charting the state space 24714.1 Qualitative dynamics . . . .
. . . . . . . . . . . . . . . . . . . . 24814.2 Stretch and fold .
. . . . . . . . . . . . . . . . . . . . . . . . . . 25114.3
Temporal ordering: Itineraries . . . . . . . . . . . . . . . . . .
. 25414.4 Spatial ordering . . . . . . . . . . . . . . . . . . . .
. . . . . . . 25614.5 Kneading theory . . . . . . . . . . . . . . .
. . . . . . . . . . . . 25714.6 Symbolic dynamics, basic notions .
. . . . . . . . . . . . . . . . 261resume 264 commentary 26414.7
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 266exercises 269 references 270
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CONTENTS v
15 Stretch, fold, prune 27315.1 Goin global: stable/unstable
manifolds . . . . . . . . . . . . . . 27415.2 Horseshoes . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 27815.3 Symbol
plane . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28115.4 Prune danish . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 28415.5 Recoding, symmetries, tilings . . . . . . . . .
. . . . . . . . . . . 28615.6 Charting the state space . . . . . .
. . . . . . . . . . . . . . . . . 288resume 291 commentary 29315.7
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 294exercises 298 references 299
16 Fixed points, and how to get them 30416.1 Where are the
cycles? . . . . . . . . . . . . . . . . . . . . . . . . 30516.2
Multipoint shooting method . . . . . . . . . . . . . . . . . . . .
30716.3 Cost function . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 309resume 311 commentary 31216.4 Examples . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 312exercises 314
references 315
II Chaos rules 317
17 Walkabout: Transition graphs 31917.1 Matrix representations
of topological dynamics . . . . . . . . . . 31917.2 Transition
graphs: wander from node to node . . . . . . . . . . . 32117.3
Transition graphs: stroll from link to link . . . . . . . . . . . .
. 323resume 325 commentary 32617.4 Examples . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 327exercises 330 references
331
18 Counting 33218.1 How many ways to get there from here? . . .
. . . . . . . . . . . 33318.2 Topological trace formula . . . . . .
. . . . . . . . . . . . . . . . 33418.3 Determinant of a graph . .
. . . . . . . . . . . . . . . . . . . . . 33718.4 Topological zeta
function . . . . . . . . . . . . . . . . . . . . . . 33918.5
Infinite partitions . . . . . . . . . . . . . . . . . . . . . . . .
. . 34118.6 Shadowing . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 34318.7 Counting cycles . . . . . . . . . . . . . . . .
. . . . . . . . . . . 344resume 346 commentary 34818.8 Examples . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
349exercises 353 references 356
19 Transporting densities 35819.1 Measures . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 35919.2 Perron-Frobenius
operator . . . . . . . . . . . . . . . . . . . . . 36019.3 Why not
just leave it to a computer? . . . . . . . . . . . . . . . .
36219.4 Invariant measures . . . . . . . . . . . . . . . . . . . .
. . . . . 36419.5 Density evolution for infinitesimal times . . . .
. . . . . . . . . . 36819.6 Liouville operator . . . . . . . . . .
. . . . . . . . . . . . . . . . 369
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CONTENTS vi
resume 371 commentary 37219.7 Examples . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 373exercises 374 references
376
20 Averaging 37820.1 Dynamical averaging . . . . . . . . . . . .
. . . . . . . . . . . . 37820.2 Moments, cumulants . . . . . . . .
. . . . . . . . . . . . . . . . 38320.3 Evolution operators . . . .
. . . . . . . . . . . . . . . . . . . . . 38520.4 Averaging in open
systems . . . . . . . . . . . . . . . . . . . . . 39020.5 Evolution
operator evaluation of Lyapunov exponents . . . . . . . 391resume
392 commentary 39320.6 Examples . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 394exercises 396 references 396
21 Trace formulas 39821.1 A trace formula for maps . . . . . . .
. . . . . . . . . . . . . . . 39921.2 A trace formula for flows . .
. . . . . . . . . . . . . . . . . . . . 40321.3 An asymptotic trace
formula . . . . . . . . . . . . . . . . . . . . 406resume 407
commentary 40821.4 Examples . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 408exercises 409 references 410
22 Spectral determinants 41122.1 Spectral determinants for maps
. . . . . . . . . . . . . . . . . . . 41122.2 Spectral determinant
for flows . . . . . . . . . . . . . . . . . . . 41222.3 Dynamical
zeta functions . . . . . . . . . . . . . . . . . . . . . . 41422.4
False zeros . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 41722.5 Spectral determinants vs. dynamical zeta functions . . .
. . . . . 41822.6 All too many eigenvalues? . . . . . . . . . . . .
. . . . . . . . . 418resume 420 commentary 42122.7 Examples . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 422exercises
424 references 426
23 Cycle expansions 42823.1 Pseudo-cycles and shadowing . . . .
. . . . . . . . . . . . . . . 42923.2 Construction of cycle
expansions . . . . . . . . . . . . . . . . . . 43223.3 Periodic
orbit averaging . . . . . . . . . . . . . . . . . . . . . . .
43623.4 Flow conservation sum rules . . . . . . . . . . . . . . . .
. . . . 43723.5 Cycle formulas for dynamical averages . . . . . . .
. . . . . . . . 43823.6 Cycle expansions for finite alphabets . . .
. . . . . . . . . . . . . 44223.7 Stability ordering of cycle
expansions . . . . . . . . . . . . . . . 443resume 445 commentary
44623.8 Examples . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 450exercises 451 references 452
24 Deterministic diusion 45424.1 Diusion in periodic arrays . .
. . . . . . . . . . . . . . . . . . . 45524.2 Diusion induced by
chains of 1-dimensional maps . . . . . . . . 45924.3 Marginal
stability and anomalous diusion . . . . . . . . . . . . . 466
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CONTENTS vii
resume 469 commentary 470 exercises 472 references 472
25 Discrete symmetry factorization 47425.1 Preview . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 47525.2 Discrete
symmetries . . . . . . . . . . . . . . . . . . . . . . . . .
47725.3 Dynamics in the fundamental domain . . . . . . . . . . . .
. . . 47825.4 Factorization of spectral determinants . . . . . . .
. . . . . . . . 48125.5 C2 = D1 factorization . . . . . . . . . . .
. . . . . . . . . . . . . 48325.6 D3 factorization: 3-disk game of
pinball . . . . . . . . . . . . . . 485resume 487 commentary
48825.7 Examples . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 488exercises 489 references 490
26 Continuous symmetry factorization 49226.1 Compact groups . .
. . . . . . . . . . . . . . . . . . . . . . . . . 49326.2
Continuous symmetries of dynamics . . . . . . . . . . . . . . . .
49926.3 Symmetry reduced trace formula for flows . . . . . . . . .
. . . . 501resume 502 commentary 50326.4 Examples . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 504exercises 507
references 507
III Chaos: what to do about it? 510
27 Why cycle? 51227.1 Escape rates . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 51227.2 Natural measure in terms of
periodic orbits . . . . . . . . . . . . 51527.3 Correlation
functions . . . . . . . . . . . . . . . . . . . . . . . . 51627.4
Trace formulas vs. level sums . . . . . . . . . . . . . . . . . . .
. 518resume 519 commentary 520 exercises 521 references 522
28 Why does it work? 52428.1 Linear maps: exact spectra . . . .
. . . . . . . . . . . . . . . . . 52528.2 Evolution operator in a
matrix representation . . . . . . . . . . . 52928.3 Classical
Fredholm theory . . . . . . . . . . . . . . . . . . . . . 53228.4
Analyticity of spectral determinants . . . . . . . . . . . . . . .
. 53428.5 Hyperbolic maps . . . . . . . . . . . . . . . . . . . . .
. . . . . 53928.6 Physics of eigenvalues and eigenfunctions . . . .
. . . . . . . . . 54128.7 Troubles ahead . . . . . . . . . . . . .
. . . . . . . . . . . . . . 543resume 544 commentary 546 exercises
548 references 548
29 Intermittency 55129.1 Intermittency everywhere . . . . . . .
. . . . . . . . . . . . . . . 55229.2 Intermittency for pedestrians
. . . . . . . . . . . . . . . . . . . . 55429.3 Intermittency for
cyclists . . . . . . . . . . . . . . . . . . . . . . 56629.4 BER
zeta functions . . . . . . . . . . . . . . . . . . . . . . . . .
573resume 576 commentary 576 exercises 578 references 579
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CONTENTS viii
30 Turbulence? 58130.1 Fluttering flame front . . . . . . . . .
. . . . . . . . . . . . . . . 58230.2 Infinite-dimensional flows:
Numerics . . . . . . . . . . . . . . . 58530.3 Visualization . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 58630.4
Equilibria of equilibria . . . . . . . . . . . . . . . . . . . . .
. . 58730.5 Why does a flame front flutter? . . . . . . . . . . . .
. . . . . . . 58930.6 Intrinsic parametrization . . . . . . . . . .
. . . . . . . . . . . . 59230.7 Energy budget . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 593resume 596 commentary 596
exercises 597 references 598
31 Irrationally winding 60131.1 Mode locking . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 60231.2 Local theory: Golden
mean renormalization . . . . . . . . . . . 60731.3 Global theory:
Thermodynamic averaging . . . . . . . . . . . . . 60931.4 Hausdor
dimension of irrational windings . . . . . . . . . . . . 61131.5
Thermodynamics of Farey tree: Farey model . . . . . . . . . . . .
613resume 615 commentary 615 exercises 618 references 619
IV The rest is noise 622
32 Noise 62432.1 Deterministic transport . . . . . . . . . . . .
. . . . . . . . . . . 62532.2 Brownian diusion . . . . . . . . . .
. . . . . . . . . . . . . . . 62632.3 Noisy trajectories . . . . .
. . . . . . . . . . . . . . . . . . . . . 62932.4 Noisy maps . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 63332.5 All
nonlinear noise is local . . . . . . . . . . . . . . . . . . . . .
63432.6 Weak noise: Hamiltonian formulation . . . . . . . . . . . .
. . . 637resume 639 commentary 639 exercises 641 references 644
33 Relaxation for cyclists 64833.1 Fictitious time relaxation .
. . . . . . . . . . . . . . . . . . . . . 64933.2 Discrete
iteration relaxation method . . . . . . . . . . . . . . . . 65433.3
Least action method . . . . . . . . . . . . . . . . . . . . . . . .
. 658resume 658 commentary 659 exercises 661 references 661
V Quantum chaos 66434 Prologue 666
34.1 Quantum pinball . . . . . . . . . . . . . . . . . . . . . .
. . . . 66734.2 Quantization of helium . . . . . . . . . . . . . .
. . . . . . . . . 669commentary 670 references 671
35 Quantum mechanics- the short short version 672exercises
675
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CONTENTS ix
36 WKB quantization 67736.1 WKB ansatz . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 67736.2 Method of stationary phase
. . . . . . . . . . . . . . . . . . . . . 68036.3 WKB quantization
. . . . . . . . . . . . . . . . . . . . . . . . . 68136.4 Beyond
the quadratic saddle point . . . . . . . . . . . . . . . . .
683resume 684 commentary 685 exercises 686 references 686
37 Semiclassical evolution 68737.1 Hamilton-Jacobi theory . . .
. . . . . . . . . . . . . . . . . . . . 68737.2 Semiclassical
propagator . . . . . . . . . . . . . . . . . . . . . . 69637.3
Semiclassical Greens function . . . . . . . . . . . . . . . . . . .
699resume 705 commentary 706 exercises 708 references 709
38 Semiclassical quantization 71038.1 Trace formula . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 71038.2 Semiclassical
spectral determinant . . . . . . . . . . . . . . . . . 71638.3
One-dof systems . . . . . . . . . . . . . . . . . . . . . . . . . .
71738.4 Two-dof systems . . . . . . . . . . . . . . . . . . . . . .
. . . . 718resume 719 commentary 720 exercises 722 references
722
39 Quantum scattering 72439.1 Density of states . . . . . . . .
. . . . . . . . . . . . . . . . . . . 72439.2 Quantum mechanical
scattering matrix . . . . . . . . . . . . . . . 72839.3
Krein-Friedel-Lloyd formula . . . . . . . . . . . . . . . . . . . .
72939.4 Wigner time delay . . . . . . . . . . . . . . . . . . . . .
. . . . . 732commentary 734 exercises 735 references 735
40 Chaotic multiscattering 73840.1 Quantum mechanical scattering
matrix . . . . . . . . . . . . . . . 73940.2 N-scatterer spectral
determinant . . . . . . . . . . . . . . . . . . 74240.3
Semiclassical 1-disk scattering . . . . . . . . . . . . . . . . . .
. 74640.4 From quantum cycle to semiclassical cycle . . . . . . . .
. . . . . 75340.5 Heisenberg uncertainty . . . . . . . . . . . . .
. . . . . . . . . . 756commentary 756 references 757
41 Helium atom 75841.1 Classical dynamics of collinear helium .
. . . . . . . . . . . . . . 75941.2 Chaos, symbolic dynamics and
periodic orbits . . . . . . . . . . . 76041.3 Local coordinates,
Jacobian matrix . . . . . . . . . . . . . . . . . 76441.4 Getting
ready . . . . . . . . . . . . . . . . . . . . . . . . . . . .
76641.5 Semiclassical quantization of collinear helium . . . . . .
. . . . . 768resume 775 commentary 775 exercises 777 references
778
42 Diraction distraction 77942.1 Quantum eavesdropping . . . . .
. . . . . . . . . . . . . . . . . 77942.2 An application . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 785resume 790
commentary 791 exercises 792 references 793
Epilogue 795
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CONTENTS x
Index 800
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CONTENTS xi
Volume www: Appendices on ChaosBook.org
VI Web Appendices 819
A A brief history of chaos 821A.1 Chaos is born . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 821A.2 Chaos grows up . . .
. . . . . . . . . . . . . . . . . . . . . . . . 825A.3 Chaos with
us . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826A.4
Periodic orbit theory . . . . . . . . . . . . . . . . . . . . . . .
. 828A.5 Dynamicists vision of turbulence . . . . . . . . . . . . .
. . . . 833A.6 Gruppenpest . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 836A.7 Death of the Old Quantum Theory . . . . .
. . . . . . . . . . . . 837commentary 840 references 841
B Go straight 849B.1 Rectification of flows . . . . . . . . . .
. . . . . . . . . . . . . . 849B.2 Collinear helium . . . . . . . .
. . . . . . . . . . . . . . . . . . 851B.3 Rectification of maps .
. . . . . . . . . . . . . . . . . . . . . . . 855B.4 Rectification
of a periodic orbit . . . . . . . . . . . . . . . . . . . 856resume
858 commentary 858 exercises 859 references 859
C Linear stability 861C.1 Linear algebra . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 861C.2 Eigenvalues and eigenvectors
. . . . . . . . . . . . . . . . . . . . 863C.3 Eigenspectra: what
to make out of them? . . . . . . . . . . . . . . 872C.4 Stability
of Hamiltonian flows . . . . . . . . . . . . . . . . . . . 872C.5
Monodromy matrix for Hamiltonian flows . . . . . . . . . . . . .
874exercises 877
D Discrete symmetries of dynamics 878D.1 Preliminaries and
definitions . . . . . . . . . . . . . . . . . . . . 878D.2
Invariants and reducibility . . . . . . . . . . . . . . . . . . . .
. 884D.3 Lattice derivatives . . . . . . . . . . . . . . . . . . .
. . . . . . . 887D.4 Periodic lattices . . . . . . . . . . . . . .
. . . . . . . . . . . . . 891D.5 Discrete Fourier transforms . . .
. . . . . . . . . . . . . . . . . . 892D.6 C4v factorization . . .
. . . . . . . . . . . . . . . . . . . . . . . . 896D.7 C2v
factorization . . . . . . . . . . . . . . . . . . . . . . . . . . .
900D.8 Henon map symmetries . . . . . . . . . . . . . . . . . . . .
. . . 902commentary 903 exercises 903 references 905
E Finding cycles 908E.1 Newton-Raphson method . . . . . . . . .
. . . . . . . . . . . . . 908E.2 Hybrid Newton-Raphson / relaxation
method . . . . . . . . . . . 909
F Symbolic dynamics techniques 912F.1 Topological zeta functions
for infinite subshifts . . . . . . . . . . 912
-
CONTENTS xii
F.2 Prime factorization for dynamical itineraries . . . . . . .
. . . . . 920
G Counting itineraries 924G.1 Counting curvatures . . . . . . .
. . . . . . . . . . . . . . . . . . 924exercises 925
H Implementing evolution 926H.1 Koopmania . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 926H.2 Implementing evolution .
. . . . . . . . . . . . . . . . . . . . . . 928commentary 931
exercises 931 references 932
I Transport of vector fields 934I.1 Evolution operator for
Lyapunov exponents . . . . . . . . . . . . 934I.2 Advection of
vector fields by chaotic flows . . . . . . . . . . . . .
939commentary 943 exercises 943 references 943
J Convergence of spectral determinants 945J.1 Curvature
expansions: geometric picture . . . . . . . . . . . . . . 945J.2 On
importance of pruning . . . . . . . . . . . . . . . . . . . . . .
948J.3 Ma-the-matical caveats . . . . . . . . . . . . . . . . . . .
. . . . 949J.4 Estimate of the nth cumulant . . . . . . . . . . . .
. . . . . . . . 950J.5 Dirichlet series . . . . . . . . . . . . . .
. . . . . . . . . . . . . 952commentary 953
K Infinite dimensional operators 954K.1 Matrix-valued functions
. . . . . . . . . . . . . . . . . . . . . . . 954K.2 Operator norms
. . . . . . . . . . . . . . . . . . . . . . . . . . . 956K.3 Trace
class and Hilbert-Schmidt class . . . . . . . . . . . . . . .
957K.4 Determinants of trace class operators . . . . . . . . . . .
. . . . . 959K.5 Von Koch matrices . . . . . . . . . . . . . . . .
. . . . . . . . . 962K.6 Regularization . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 964exercises 966 references 966
L Thermodynamic formalism 968L.1 Renyi entropies . . . . . . . .
. . . . . . . . . . . . . . . . . . . 968L.2 Fractal dimensions . .
. . . . . . . . . . . . . . . . . . . . . . . 973resume 977
commentary 977 exercises 978 references 978
M Statistical mechanics recycled 980M.1 The thermodynamic limit
. . . . . . . . . . . . . . . . . . . . . . 980M.2 Ising models . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 983M.3 Fisher
droplet model . . . . . . . . . . . . . . . . . . . . . . . .
986M.4 Scaling functions . . . . . . . . . . . . . . . . . . . . .
. . . . . 991M.5 Geometrization . . . . . . . . . . . . . . . . . .
. . . . . . . . . 994resume 1001 commentary 1002 exercises 1002
references 1003
N Noise/quantum corrections 1005N.1 Periodic orbits as
integrable systems . . . . . . . . . . . . . . . . 1005N.2 The
Birkho normal form . . . . . . . . . . . . . . . . . . . . .
1009N.3 Bohr-Sommerfeld quantization of periodic orbits . . . . . .
. . . 1010
-
CONTENTS xiii
N.4 Quantum calculation of corrections . . . . . . . . . . . . .
. . . 1012references 1018
O Projects 1021O.1 Deterministic diusion, zig-zag map . . . . .
. . . . . . . . . . . 1023references 1028O.2 Deterministic diusion,
sawtooth map . . . . . . . . . . . . . . . 1029
-
CONTENTS xiv
ContributorsNo man but a blockhead ever wrote except for
money
Samuel Johnson
This book is a result of collaborative labors of many people
over a span of severaldecades. Coauthors of a chapter or a section
are indicated in the byline to thechapter/section title. If you are
referring to a specific coauthored section ratherthan the entire
book, cite it as (for example):
C. Chandre, F.K. Diakonos and P. Schmelcher, section Discrete
cyclist re-laxation method, in P. Cvitanovic, R. Artuso, R.
Mainieri, G. Tanner andG. Vattay, Chaos: Classical and Quantum
(Niels Bohr Institute, Copen-hagen 2010);
ChaosBook.org/version13.
Do not cite chapters by their numbers, as those change from
version to version.Chapters without a byline are written by Predrag
Cvitanovic. Friends whose con-tributions and ideas were invaluable
to us but have not contributed written text tothis book, are
credited in the acknowledgments.
Roberto Artuso19 Transporting densities . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35821.2 A
trace formula for flows . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 40327.3 Correlation functions . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51629 Intermittency . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 55124 Deterministic
diusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 454
Ronnie Mainieri2 Flows . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 373.2 The Poincare section of a flow . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 714 Local stability . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 84B.1 Understanding flows . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85014.1
Temporal ordering: itineraries . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 248Appendix A: A brief history of chaos .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 821
Gabor VattayGregor Tanner
29 Intermittency . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 551Appendix C.5:
Jacobians of Hamiltonian flows . . . . . . . . . . . . . . . . . .
. . 874
Arindam BasuRossler flow figures, tables, cycles in chapters 14,
16 and exercise 7.1
Ofer Biham33.1 Cyclists relaxation method . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 649
Daniel Borrero Oct 23 2008, soluCycles.tex
Solution 16.1N. Burak Budanur
-
CONTENTS xv
0.113.5 First Fourier mode slice . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 22913.6 Stability within
a slice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 229Solution 12.5Solution 12.6
Cristel Chandre33.1 Cyclists relaxation method . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 64933.2 Discrete
cyclists relaxation methods . . . . . . . . . . . . . . . . . . . .
. . . . . 654
Freddy Christiansen
7.1 One-dimensional mappings . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 13416.2 Multipoint shooting method
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
.307
Per Dahlqvist
29 Intermittency . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 55133.3 Orbit
length extremization method for billiards . . . . . . . . . . . . .
. . 658
Carl P. Dettmann23.7 Stability ordering of cycle expansions . .
. . . . . . . . . . . . . . . . . . . . . .443
Fotis K. Diakonos33.2 Discrete cyclists relaxation methods . . .
. . . . . . . . . . . . . . . . . . . . . . 654
G. Bard ErmentroutExercise 5.1
Mitchell J. FeigenbaumAppendix C.4: Symplectic invariance . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 872
Sarah Flynn
solutions 3.5 and 3.6Matjaz Gomilsek
17.3 Transition graphs: stroll from link to link . . . . . . . .
. . . . . . . . . . . . .323
Jonathan HalcrowExample 3.4: Sections of Lorenz flow . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 79Example 4.6:
Stability of Lorenz flow equilibria . . . . . . . . . . . . . . . .
. . . 100Example 4.7: Lorenz flow: Global portrait . . . . . . . .
. . . . . . . . . . . . . . . . 102Example 11.5: Desymmetrization
of Lorenz flow . . . . . . . . . . . . . . . . . . 194Example 14.5:
Lorenz flow: a 1-dimensional return map . . . . . . . . . . .
266Exercises 11.4 and figure 2.5
Kai T. Hansen14.3 Unimodal map symbolic dynamics . . . . . . . .
. . . . . . . . . . . . . . . . . . 25418.5 Topological zeta
function for an infinite partition . . . . . . . . . . . . .
.34114.5 Kneading theory . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 257figures throughout
the text
-
CONTENTS xvi
Rainer Klages
Figure 24.5
Yueheng Lan
Solutions 1.1, 2.2, 2.3, 2.4, 2.5, 11.1, 15.7, 14.6, 19.1, 19.2,
19.3, 19.5,19.7, 19.10, 6.3 and figures 1.9, 11.1, 11.5 14.5,
Bo LiSolutions 35.2, 35.1, 36.1
Norman LebovitzExample 15.2 A simple stable/unstable manifolds
pair . . . . . . . . . . . . . 294
Joachim Mathiesen6.2 Lyapunov exponents . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Rossler
flow figures, tables, cycles in sect. 6.2 and exercise 7.1
Yamato MatsuokaFigure 15.4
Radford Mitchell, Jr.Example 3.5
Rytis Paskauskas
4.6 Stability of Poincare return maps . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 925.5 Stability of Poincare map
cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
115Exercises 2.8, 3.1, 4.4 and solution 4.1
Adam Prugel-Bennet
Solutions 1.2, 2.10, 9.1, 20.2, 23.2 28.3, 33.1.Lamberto
Rondoni
19 Transporting densities . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 35816.1.1 Cycles from
long time series . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 30527.2.1 Unstable periodic orbits are dense . . . . . .
. . . . . . . . . . . . . . . . . . . 515Table 18.2
Juri RolfSolution 28.3
Per E. Rosenqvist
exercises, figures throughout the text
Hans Henrik Rugh
28 Why does it work? . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 524
Luis Saldanasolution 11.2
Peter Schmelcher33.2 Discrete cyclists relaxation methods . . .
. . . . . . . . . . . . . . . . . . . . . . 654
-
CONTENTS xvii
Evangelos Siminos
Example 3.4: Sections of Lorenz flow . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 79Example 4.6: Stability of Lorenz
flow equilibria . . . . . . . . . . . . . . . . . . . 100Example
4.7: Lorenz flow: Global portrait . . . . . . . . . . . . . . . . .
. . . . . . . 102Example 11.5: Desymmetrization of Lorenz flow . .
. . . . . . . . . . . . . . . . 194Example 14.5: Lorenz flow: a
1-dimensional return map . . . . . . . . . . . 266Exercise
11.4Solution 13.1
Gabor SimonRossler flow figures, tables, cycles in chapters 2,
16 and exercise 7.1
Edward A. Spiegel
2 Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3719
Transporting densities . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 358
Luz V. Vela-Arevalo8.1 Hamiltonian flows . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
145Exercises 8.1, 8.3, 8.5
Lei Zhang
Solutions 1.1, 2.1
-
CONTENTS xviii
Acknowledgments
I feel I never want to write another book. Whats the good!I can
eke living on stories and little articles, that dont costa tithe of
the output a book costs. Why write novels anymore!
D.H. Lawrence
This book owes its existence to the Niels Bohr Institutes and
Norditas hos-pitable and nurturing environment, and the private,
national and cross-nationalfoundations that have supported the
collaborators research over a span of severaldecades. P.C. thanks
M.J. Feigenbaum of Rockefeller University; D. Ruelle ofI.H.E.S.,
Bures-sur-Yvette; I. Procaccia of Minerva Center for Nonlinear
Physicsof Complex Systems, Weizmann Institute of Science; P.H.
Damgaard of the NielsBohr International Academy; G. Mazenko of U.
of Chicago James Franck Insti-tute and Argonne National Laboratory;
T. Geisel of Max-Planck-Institut fur Dy-namik und
Selbstorganisation, Gottingen; I. Andric of Rudjer Boskovic
Institute;P. Hemmer of University of Trondheim; The Max-Planck
Institut fur Mathematik,Bonn; J. Lowenstein of New York University;
Edificio Celi, Milano; Fundacaode Faca, Porto Seguro; and Dr. Dj.
Cvitanovic, Kostrena, for the hospitality dur-ing various stages of
this work, and the Carlsberg Foundation, Glen P. Robinson,Humboldt
Foundation and National Science Fundation grant DMS-0807574
forpartial support.
The authors gratefully acknowledge collaborations and/or
stimulating discus-sions with E. Aurell, M. Avila, V. Baladi, D.
Barkley, B. Brenner, G. Byrne,A. de Carvalho, D.J. Driebe, B.
Eckhardt, M.J. Feigenbaum, J. Frjland, S. Froehlich,P. Gaspar, P.
Gaspard, J. Guckenheimer, G.H. Gunaratne, P. Grassberger, H.
Gutowitz,M. Gutzwiller, K.T. Hansen, P.J. Holmes, T. Janssen, R.
Klages, T. Kreilos, Y. Lan,B. Lauritzen, C. Marcotte, J. Milnor, M.
Nordahl, I. Procaccia, J.M. Robbins,P.E. Rosenqvist, D. Ruelle, G.
Russberg, B. Sandstede, A. Shapere, M. Sieber,D. Sullivan, N.
Sndergaard, T. Tel, C. Tresser, R. Wilczak, and D. Wintgen.
We thank Dorte Glass, Tzatzilha Torres Guadarrama and Raenell
Soller fortyping parts of the manuscript; D. Borrero, P. Duren, B.
Lautrup, J.F Gibson,M. Gomilsek and D. Viswanath for comments and
corrections to the prelimi-nary versions of this text; M.A. Porter
for patiently and critically reading themanuscript, and then
lengthening by the 2013 definite articles hitherto missing;M.V.
Berry for the quotation on page 821; H. Fogedby for the quotation
onpage 534; J. Greensite for the quotation on page 7; S. Ortega
Arango for the quo-tation on page 16; Ya.B. Pesin for the remarks
quoted on page 841; M.A. Porterfor the quotations on pages 8.1, 20,
16, 1.6 and A.4; E.A. Spiegel for quotationon page 3; and E.
Valesco for the quotation on page 25.
F. Haakes heartfelt lament on page 403 was uttered at the end of
the firstconference presentation of cycle expansions, in 1988. G.P.
Morriss advice tostudents as how to read the introduction to this
book, page6, was oered duringa 2002 graduate course in Dresden. J.
Bellissards advice to students concerningunpleasant operators and
things nonlinear, pages4.3 and 20.3.1, was shared in his2013
Classical Mechanics II lectures on manifolds. K. Huangs C.N. Yang
in-terview quoted on page 365 is available on ChaosBook.org/extras.
T.D. Leeremarks on as to who is to blame, page 37 and page 305, as
well as M. Shubs
-
CONTENTS xix
helpful technical remark on page 546 came during the Rockefeller
University De-cember 2004 Feigenbaum Fest. Quotes on pages 37, 144,
and 362 are takenfrom a book review by J. Guckenheimer [1].
Who is the 3-legged dog reappearing throughout the book? Long
ago, whenwe were innocent and knew not Borel measurable to sets, P.
Cvitanovic askedV. Baladi a question about dynamical zeta
functions, who then asked J.-P. Eck-mann, who then asked D. Ruelle.
The answer was transmitted back: The mastersays: It is holomorphic
in a strip. Hence His Masters Voice logo, and the 3-legged dog is
us, still eager to fetch the bone. The answer has made it to the
book,though not precisely in His Masters voice. As a matter of
fact, the answer is thebook. We are still chewing on it.
What about the two beers? During his PhD studies, R. Artuso
found thesmrrebrd at the Niels Bohr Institute indigestible, so he
digested H.M.V.s wis-dom on a strict diet of two Carlsbergs and two
pieces of danish pastry for lunchevery day, as depicted on the
cover. Frequent trips back to Milano family kept himalivehe never
got desperate enough to try the Danish smrrebrd. And the
cyclewheel? Well, this is no book for pedestrians.
And last but not least: profound thanks to all the unsung heroes
students andcolleagues, too numerous to list here who have
supported this project over manyyears in many ways, by surviving
pilot courses based on this book, by providinginvaluable insights,
by teaching us, by inspiring us.
-
Part I
Geometry of chaos
1
-
2We start out with a recapitulation of the basic notions of
dynamics. Our aim isnarrow; we keep the exposition focused on
prerequisites to the applications tobe developed in this text. We
assume that the reader is familiar with dynamicson the level of the
introductory texts mentioned in remark 1.1, and concentrate here
ondeveloping intuition about what a dynamical system can do. It
will be a broad strokedescription, since describing all possible
behaviors of dynamical systems is beyondhuman ken. While for a
novice there is no shortcut through this lengthy detour,
asophisticated traveler might bravely skip this well-trodden
territory and embark upon thejourney at chapter 18.
The fate has handed you a flow. What are you to do about it?
1. Define your dynamical system (M, f ): the space M of its
possible states, and thelaw f t of their evolution in time.
2. Pin it down locallyis there anything about it that is
stationary? Try to determine itsequilibria / fixed points (chapter
2).
3. Cut across it, represent as a Poincare map from a section to
a section (chapter3).4. Explore the neighborhood by linearizing the
flow; check the linear stability of its
equilibria / fixed points, their stability eigen-directions
(chapters4 and 5).5. Does your system have a symmetry? If so, you
must use it (chapters10 to 12). Slice
& dice it (chapter 13).6. Go global: train by partitioning
the state space of 1-dimensional maps. Label the
regions by symbolic dynamics (chapter 14).7. Now venture global
distances across the system by continuing local tangent space
into stable / unstable manifolds. Their intersections partition
the state space in adynamically invariant way (chapter 15).
8. Guided by this topological partition, compute a set of
periodic orbits up to a giventopological length (chapter 7 and
chapter 16).
Along the way you might want to learn about Lyapunov exponents
(chapter6), classicalmechanics (chapter 8), and billiards (chapter
9).
ackn.tex 12decd2010ChaosBook.org version15.6, Mar 15 2015
-
Chapter 1
Overture
If I have seen less far than other men it is because I havestood
behind giants.
Edoardo Specchio
Rereading classic theoretical physics textbooks leaves a sense
that there areholes large enough to steam a Eurostar train through
them. Here we learnabout harmonic oscillators and Keplerian
ellipses - but where is the chap-ter on chaotic oscillators, the
tumbling Hyperion? We have just quantized hydro-gen, where is the
chapter on the classical 3-body problem and its implications
forquantization of helium? We have learned that an instanton is a
solution of field-theoretic equations of motion, but shouldnt a
strongly nonlinear field theory haveturbulent solutions? How are we
to think about systems where things fall apart;the center cannot
hold; every trajectory is unstable?
This chapter oers a quick survey of the main topics covered in
the book.Throughout the book
indicates that the section is on a pedestrian level - you are
expected toknow/learn this material
indicates that the section is on a somewhat advanced, cyclist
level
indicates that the section requires a hearty stomach and is
probably bestskipped on first reading
fast track points you where to skip to
tells you where to go for more depth on a particular topic
link to a related video
[exercise 1.2] on margin links to an exercise that might clarify
a point in the text
3
-
CHAPTER 1. OVERTURE 4
indicates that a figure is still missingyou are urged to fetch
it
We start out by making promiseswe will right wrongs, no longer
shall you suerthe slings and arrows of outrageous Science of
Perplexity. We relegate a historicaloverview of the development of
chaotic dynamics to appendixA, and head straightto the starting
line: A pinball game is used to motivate and illustrate most of
theconcepts to be developed in ChaosBook.
This is a textbook, not a research monograph, and you should be
able to followthe thread of the argument without constant
excursions to sources. Hence there areno literature references in
the text proper, all learned remarks and bibliographicalpointers
are relegated to the Commentary section at the end of each
chapter.
1.1 Why ChaosBook?
It seems sometimes that through a preoccupation with sci-ence,
we acquire a firmer hold over the vicissitudes of lifeand meet them
with greater calm, but in reality we havedone no more than to find
a way to escape from our sor-rows.
Hermann Minkowski in a letter to David Hilbert
The problem has been with us since Newtons first frustrating
(and unsuccessful)crack at the 3-body problem, lunar dynamics.
Nature is rich in systems governedby simple deterministic laws
whose asymptotic dynamics are complex beyondbelief, systems which
are locally unstable (almost) everywhere but globally recur-rent.
How do we describe their long term dynamics?
The answer turns out to be that we have to evaluate a
determinant, take alogarithm. It would hardly merit a learned
treatise, were it not for the fact that thisdeterminant that we are
to compute is fashioned out of infinitely many infinitelysmall
pieces. The feel is of statistical mechanics, and that is how the
problemwas solved; in the 1960s the pieces were counted, and in the
1970s they wereweighted and assembled in a fashion that in beauty
and in depth ranks along withthermodynamics, partition functions
and path integrals amongst the crown jewelsof theoretical
physics.
This book is not a book about periodic orbits. The red thread
throughout thetext is the duality between the local, topological,
short-time dynamically invariantcompact sets (equilibria, periodic
orbits, partially hyperbolic invariant tori) andthe global
long-time evolution of densities of trajectories. Chaotic dynamics
isgenerated by the interplay of locally unstable motions, and the
interweaving oftheir global stable and unstable manifolds. These
features are robust and acces-sible in systems as noisy as slices
of rat brains. Poincare, the first to understanddeterministic
chaos, already said as much (modulo rat brains). Once this
topology
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CHAPTER 1. OVERTURE 5
is understood, a powerful theory yields the observable
consequences of chaoticdynamics, such as atomic spectra, transport
coecients, gas pressures.
That is what we will focus on in ChaosBook. The book is a
self-containedgraduate textbook on classical and quantum chaos.
Your professor does not knowthis material, so you are on your own.
We will teach you how to evaluate a deter-minant, take a
logarithmstu like that. Ideally, this should take 100 pages or
so.Well, we failso far we have not found a way to traverse this
material in less thana semester, or 200-300 page subset of this
text. Nothing to be done.
1.2 Chaos ahead
Things fall apart; the centre cannot hold.W.B. Yeats, The Second
Coming
The study of chaotic dynamics is no recent fashion. It did not
start with thewidespread use of the personal computer. Chaotic
systems have been studied forover 200 years. During this time many
have contributed, and the field followed nosingle line of
development; rather one sees many interwoven strands of
progress.
In retrospect many triumphs of both classical and quantum
physics were astroke of luck: a few integrable problems, such as
the harmonic oscillator andthe Kepler problem, though non-generic,
have gotten us very far. The successhas lulled us into a habit of
expecting simple solutions to simple equationsanexpectation
tempered by our recently acquired ability to numerically scan the
statespace of non-integrable dynamical systems. The initial
impression might be thatall of our analytic tools have failed us,
and that the chaotic systems are amenableonly to numerical and
statistical investigations. Nevertheless, a beautiful theoryof
deterministic chaos, of predictive quality comparable to that of
the traditionalperturbation expansions for nearly integrable
systems, already exists.
In the traditional approach the integrable motions are used as
zeroth-order ap-proximations to physical systems, and weak
nonlinearities are then accounted forperturbatively. For strongly
nonlinear, non-integrable systems such expansionsfail completely;
at asymptotic times the dynamics exhibits amazingly rich struc-ture
which is not at all apparent in the integrable approximations.
However, hiddenin this apparent chaos is a rigid skeleton, a
self-similar tree of cycles (periodic or-bits) of increasing
lengths. The insight of the modern dynamical systems theoryis that
the zeroth-order approximations to the harshly chaotic dynamics
should bevery dierent from those for the nearly integrable systems:
a good starting ap-proximation here is the stretching and folding
of bakers dough, rather than theperiodic motion of a harmonic
oscillator.
So, what is chaos, and what is to be done about it? To get some
feeling for howand why unstable cycles come about, we start by
playing a game of pinball. Theremainder of the chapter is a quick
tour through the material covered in Chaos-Book. Do not worry if
you do not understand every detail at the first readingthe
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CHAPTER 1. OVERTURE 6
Figure 1.1: A physicists bare bones game of pinball.
intention is to give you a feeling for the main themes of the
book. Details willbe filled out later. If you want to get a
particular point clarified right now, [section
section 1.41.4] on the margin points at the appropriate
section.
1.3 The future as in a mirror
All you need to know about chaos is contained in the
intro-duction of [ChaosBook]. However, in order to understandthe
introduction you will first have to read the rest of thebook.
Gary Morriss
That deterministic dynamics leads to chaos is no surprise to
anyone who has triedpool, billiards or snookerthe game is about
beating chaosso we start our storyabout what chaos is, and what to
do about it, with a game of pinball. This mightseem a trifle, but
the game of pinball is to chaotic dynamics what a pendulum isto
integrable systems: thinking clearly about what chaos in a game of
pinballis will help us tackle more dicult problems, such as
computing the diusionconstant of a deterministic gas, the drag
coecient of a turbulent boundary layer,or the helium spectrum.
We all have an intuitive feeling for what a ball does as it
bounces among thepinball machines disks, and only high-school level
Euclidean geometry is neededto describe its trajectory. A
physicists pinball game is the game of pinball strip-ped to its
bare essentials: three equidistantly placed reflecting disks in a
plane,figure 1.1. A physicists pinball is free, frictionless,
point-like, spin-less, perfectlyelastic, and noiseless. Point-like
pinballs are shot at the disks from random startingpositions and
angles; they spend some time bouncing between the disks and
thenescape.
At the beginning of the 18th century Baron Gottfried Wilhelm
Leibniz wasconfident that given the initial conditions one knew
everything a deterministicsystem would do far into the future. He
wrote [2], anticipating by a century anda half the oft-quoted
Laplaces Given for one instant an intelligence which
couldcomprehend all the forces by which nature is animated...:
That everything is brought forth through an established destiny
is just
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CHAPTER 1. OVERTURE 7
Figure 1.2: Sensitivity to initial conditions: two pin-balls
that start out very close to each other separate ex-ponentially
with time.
1
2
3
23132321
2313
as certain as that three times three is nine. [. . . ] If, for
example, one spheremeets another sphere in free space and if their
sizes and their paths anddirections before collision are known, we
can then foretell and calculatehow they will rebound and what
course they will take after the impact. Verysimple laws are
followed which also apply, no matter how many spheresare taken or
whether objects are taken other than spheres. From this onesees
then that everything proceeds mathematicallythat is, infalliblyin
thewhole wide world, so that if someone could have a sucient
insight intothe inner parts of things, and in addition had
remembrance and intelligenceenough to consider all the
circumstances and to take them into account, hewould be a prophet
and would see the future in the present as in a mirror.
Leibniz chose to illustrate his faith in determinism precisely
with the type of phys-ical system that we shall use here as a
paradigm of chaos. His claim is wrong in adeep and subtle way: a
state of a physical system can never be specified to
infiniteprecision, and by this we do not mean that eventually the
Heisenberg uncertaintyprinciple kicks in. In the classical,
deterministic dynamics there is no way to takeall the circumstances
into account, and a single trajectory cannot be tracked, onlya ball
of nearby initial points makes physical sense.
1.3.1 What is chaos?
I accept chaos. I am not sure that it accepts me.Bob Dylan,
Bringing It All Back Home
A deterministic system is a system whose present state is in
principle fully deter-mined by its initial conditions.
In contrast, radioactive decay, Brownian motion and heat flow
are examplesof stochastic systems, for which the initial conditions
determine the future onlypartially, due to noise, or other external
circumstances beyond our control: thepresent state reflects the
past initial conditions plus the particular realization ofthe noise
encountered along the way.
A deterministic system with suciently complicated dynamics can
appear tous to be stochastic; disentangling the deterministic from
the stochastic is the main
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CHAPTER 1. OVERTURE 8
Figure 1.3: Unstable trajectories separate with time. x(0)
x(t)
x(t)x(0)
challenge in many real-life settings, from stock markets to
palpitations of chickenhearts. So, what is chaos?
In a game of pinball, any two trajectories that start out very
close to each otherseparate exponentially with time, and in a
finite (and in practice, a very small)number of bounces their
separation x(t) attains the magnitude of L, the charac-teristic
linear extent of the whole system, figure 1.2. This property of
sensitivityto initial conditions can be quantified as
|x(t)| et |x(0)|
where , the mean rate of separation of trajectories of the
system, is called theLyapunov exponent. For any finite accuracy x =
|x(0)| of the initial data, the
chapter 6dynamics is predictable only up to a finite Lyapunov
time
TLyap 1 ln |x/L| , (1.1)
despite the deterministic and, for Baron Leibniz, infallible
simple laws that rulethe pinball motion.
A positive Lyapunov exponent does not in itself lead to chaos.
One could tryto play 1- or 2-disk pinball game, but it would not be
much of a game; trajecto-ries would only separate, never to meet
again. What is also needed is mixing, thecoming together again and
again of trajectories. While locally the nearby trajec-tories
separate, the interesting dynamics is confined to a globally finite
region ofthe state space and thus the separated trajectories are
necessarily folded back andcan re-approach each other arbitrarily
closely, infinitely many times. For the caseat hand there are 2n
topologically distinct n bounce trajectories that originate froma
given disk. More generally, the number of distinct trajectories
with n bouncescan be quantified as
section 18.1
N(n) ehn
where h, the growth rate of the number of topologically distinct
trajectories, iscalled the topological entropy (h = ln 2 in the
case at hand).
The appellation chaos is a confusing misnomer, as in
deterministic dynam-ics there is no chaos in the everyday sense of
the word; everything proceedsmathematicallythat is, as Baron
Leibniz would have it, infallibly. When a physi-cist says that a
certain system exhibits chaos, he means that the system obeys
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CHAPTER 1. OVERTURE 9
Figure 1.4: Dynamics of a chaotic dynamical sys-tem is (a)
everywhere locally unstable (positiveLyapunov exponent) and (b)
globally mixing (pos-itive entropy). (A. Johansen)
(a) (b)
deterministic laws of evolution, but that the outcome is highly
sensitive to smalluncertainties in the specification of the initial
state. The word chaos has in thiscontext taken on a narrow
technical meaning. If a deterministic system is locallyunstable
(positive Lyapunov exponent) and globally mixing (positive
entropy)figure 1.4it is said to be chaotic.
While mathematically correct, the definition of chaos as
positive Lyapunov+ positive entropy is useless in practice, as a
measurement of these quantities isintrinsically asymptotic and
beyond reach for systems observed in nature. Morepowerful is
Poincares vision of chaos as the interplay of local instability
(unsta-ble periodic orbits) and global mixing (intertwining of
their stable and unstablemanifolds). In a chaotic system any open
ball of initial conditions, no matter howsmall, will in a finite
time overlap with any other finite region and in this sensespread
over the extent of the entire asymptotically accessible state
space. Oncethis is grasped, the focus of theory shifts from
attempting to predict individualtrajectories (which is impossible)
to a description of the geometry of the spaceof possible outcomes,
and evaluation of averages over this space. How this isaccomplished
is what ChaosBook is about.
A definition of turbulence is even harder to come by. Can you
recognizeturbulence when you see it? The word comes from
tourbillon, French for vor-tex, and intuitively it refers to
irregular behavior of spatially extended systemdescribed by
deterministic equations of motionsay, a bucket of sloshing
waterdescribed by the Navier-Stokes equations. But in practice the
word turbulencetends to refer to messy dynamics which we understand
poorly. As soon as aphenomenon is understood better, it is
reclaimed and renamed: a route to chaos,spatiotemporal chaos, and
so on.
In ChaosBook we shall develop a theory of chaotic dynamics for
low dimens-ional attractors visualized as a succession of nearly
periodic but unstable motions.In the same spirit, we shall think of
turbulence in spatially extended systems interms of recurrent
spatiotemporal patterns. Pictorially, dynamics drives a
givenspatially extended system (clouds, say) through a repertoire
of unstable patterns;as we watch a turbulent system evolve, every
so often we catch a glimpse of afamiliar pattern:
= other swirls =
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CHAPTER 1. OVERTURE 10
For any finite spatial resolution, a deterministic flow follows
approximately for afinite time an unstable pattern belonging to a
finite alphabet of admissible patterns,and the long term dynamics
can be thought of as a walk through the space of suchpatterns. In
ChaosBook we recast this image into mathematics.
1.3.2 When does chaos matter?
In dismissing Pollocks fractals because of their
limitedmagnification range, Jones-Smith and Mathur would
alsodismiss half the published investigations of physical
frac-tals.
Richard P. Taylor [4, 5]
When should we be mindful of chaos? The solar system is chaotic,
yet wehave no trouble keeping track of the annual motions of
planets. The rule of thumbis this; if the Lyapunov time (1.1)the
time by which a state space region initiallycomparable in size to
the observational accuracy extends across the entire acces-sible
state spaceis significantly shorter than the observational time,
you need tomaster the theory that will be developed here. That is
why the main successes ofthe theory are in statistical mechanics,
quantum mechanics, and questions of longterm stability in celestial
mechanics.
In science popularizations too much has been made of the impact
of chaostheory, so a number of caveats are already needed at this
point.
At present the theory that will be developed here is in practice
applicable onlyto systems of a low intrinsic dimension the minimum
number of coordinates nec-essary to capture its essential dynamics.
If the system is very turbulent (a descrip-tion of its long time
dynamics requires a space of high intrinsic dimension) we areout of
luck. Hence insights that the theory oers in elucidating problems
of fullydeveloped turbulence, quantum field theory of strong
interactions and early cos-mology have been modest at best. Even
that is a caveat with qualifications. Thereare applicationssuch as
spatially extended (non-equilibrium) systems, plumbersturbulent
pipes, etc.,where the few important degrees of freedom can be
isolatedand studied profitably by methods to be described here.
Thus far the theory has had limited practical success when
applied to the verynoisy systems so important in the life sciences
and in economics. Even thoughwe are often interested in phenomena
taking place on time scales much longerthan the intrinsic time
scale (neuronal inter-burst intervals, cardiac pulses,
etc.),disentangling chaotic motions from the environmental noise
has been very hard.
In 1980s something happened that might be without parallel; this
is an areaof science where the advent of cheap computation had
actually subtracted fromour collective understanding. The computer
pictures and numerical plots of frac-tal science of the 1980s have
overshadowed the deep insights of the 1970s, andthese pictures have
since migrated into textbooks. By a regrettable oversight,
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CHAPTER 1. OVERTURE 11
Figure 1.5: Katherine Jones-Smith, Untitled 5, thedrawing used
by K. Jones-Smith and R.P. Taylor to testthe fractal analysis of
Pollocks drip paintings [6].
ChaosBook has none, so Untitled 5 of figure 1.5 will have to do
as the illustra-tion of the power of fractal analysis. Fractal
science posits that certain quantities
remark 1.7(Lyapunov exponents, generalized dimensions, . . . )
can be estimated on a com-puter. While some of the numbers so
obtained are indeed mathematically sensiblecharacterizations of
fractals, they are in no sense observable and measurable onthe
length-scales and time-scales dominated by chaotic dynamics.
Even though the experimental evidence for the fractal geometry
of nature iscircumstantial [7], in studies of probabilistically
assembled fractal aggregates weknow of nothing better than
contemplating such quantities. In deterministic sys-tems we can do
much better.
1.4 A game of pinball
Formulas hamper the understanding.S. Smale
We are now going to get down to the brass tacks. Time to fasten
your seat beltsand turn o all electronic devices. But first, a
disclaimer: If you understand therest of this chapter on the first
reading, you either do not need this book, or you aredelusional. If
you do not understand it, it is not because the people who
figuredall this out first are smarter than you: the most you can
hope for at this stage is toget a flavor of what lies ahead. If a
statement in this chapter mystifies/intrigues,fast forward to a
section indicated by [section ...] on the margin, read only
theparts that you feel you need. Of course, we think that you need
to learn ALL of it,or otherwise we would not have included it in
ChaosBook in the first place.
Confronted with a potentially chaotic dynamical system, our
analysis pro-ceeds in three stages; I. diagnose, II. count, III.
measure. First, we determine
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CHAPTER 1. OVERTURE 12
Figure 1.6: Binary labeling of the 3-disk pinball tra-jectories;
a bounce in which the trajectory returns tothe preceding disk is
labeled 0, and a bounce whichresults in continuation to the third
disk is labeled 1.
the intrinsic dimension of the systemthe minimum number of
coordinates nec-essary to capture its essential dynamics. If the
system is very turbulent we are,at present, out of luck. We know
only how to deal with the transitional regimebetween regular
motions and chaotic dynamics in a few dimensions. That is
stillsomething; even an infinite-dimensional system such as a
burning flame front canturn out to have a very few chaotic degrees
of freedom. In this regime the chaoticdynamics is restricted to a
space of low dimension, the number of relevant param-eters is
small, and we can proceed to step II; we count and classify all
possible
chapter 14chapter 18topologically distinct trajectories of the
system into a hierarchy whose successive
layers require increased precision and patience on the part of
the observer. Thiswe shall do in sect. 1.4.2. If successful, we can
proceed with step III: investigatethe weights of the dierent pieces
of the system.
We commence our analysis of the pinball game with steps I, II:
diagnose,count. We shall return to step IIImeasurein sect. 1.5. The
three sections that
chapter 23follow are highly technical, they go into the guts of
what the book is about. Iftoday is not your thinking day, skip
them, jump straight to sect.1.7.
1.4.1 Symbolic dynamics
With the game of pinball we are in luckit is a low dimensional
system, freemotion in a plane. The motion of a point particle is
such that after a collisionwith one disk it either continues to
another disk or it escapes. If we label thethree disks by 1, 2 and
3, we can associate every trajectory with an itinerary, asequence
of labels indicating the order in which the disks are visited; for
example,the two trajectories in figure 1.2 have itineraries 2313 ,
23132321 respectively.
exercise 1.1section 2.1Such labeling goes by the name symbolic
dynamics. As the particle cannot collide
two times in succession with the same disk, any two consecutive
symbols mustdier. This is an example of pruning, a rule that
forbids certain subsequencesof symbols. Deriving pruning rules is
in general a dicult problem, but with thegame of pinball we are
luckyfor well-separated disks there are no further
pruningrules.
chapter 15
The choice of symbols is in no sense unique. For example, as at
each bouncewe can either proceed to the next disk or return to the
previous disk, the above3-letter alphabet can be replaced by a
binary {0, 1} alphabet, figure1.6. A cleverchoice of an alphabet
will incorporate important features of the dynamics, such asits
symmetries.
section 14.6
Suppose you wanted to play a good game of pinball, that is, get
the pinballto bounce as many times as you possibly canwhat would be
a winning strategy?The simplest thing would be to try to aim the
pinball so it bounces many times
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CHAPTER 1. OVERTURE 13
Figure 1.7: The 3-disk pinball cycles 12323 and121212313.
Figure 1.8: (a) A trajectory starting out from disk1 can either
hit another disk or escape. (b) Hittingtwo disks in a sequence
requires a much sharper aim,with initial conditions that hit
further consecutive disksnested within each other, as in Fig.
1.9.
between a pair of disksif you managed to shoot it so it starts
out in the periodicorbit bouncing along the line connecting two
disk centers, it would stay there for-ever. Your game would be just
as good if you managed to get it to keep bouncingbetween the three
disks forever, or place it on any periodic orbit. The only rubis
that any such orbit is unstable, so you have to aim very accurately
in order tostay close to it for a while. So it is pretty clear that
if one is interested in playingwell, unstable periodic orbits are
importantthey form the skeleton onto which alltrajectories trapped
for long times cling.
1.4.2 Partitioning with periodic orbits
A trajectory is periodic if it returns to its starting position
and momentum. Weshall sometimes refer to the set of periodic points
that belong to a given periodicorbit as a cycle.
Short periodic orbits are easily drawn and enumeratedan example
is drawn infigure 1.7but it is rather hard to perceive the
systematics of orbits from their con-figuration space shapes. In
mechanics a trajectory is fully and uniquely specifiedby its
position and momentum at a given instant, and no two distinct state
spacetrajectories can intersect. Their projections onto arbitrary
subspaces, however,can and do intersect, in rather unilluminating
ways. In the pinball example theproblem is that we are looking at
the projections of a 4-dimensional state spacetrajectories onto a
2-dimensional subspace, the configuration space. A clearerpicture
of the dynamics is obtained by constructing a set of state space
Poincaresections.
Suppose that the pinball has just bounced o disk 1. Depending on
its positionand outgoing angle, it could proceed to either disk 2
or 3. Not much happens inbetween the bouncesthe ball just travels
at constant velocity along a straight lineso we can reduce the
4-dimensional flow to a 2-dimensional map P that takes
thecoordinates of the pinball from one disk edge to another disk
edge. The trajectoryjust after the moment of impact is defined by
sn, the arc-length position of thenth bounce along the billiard
wall, and pn = p sin n the momentum component
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CHAPTER 1. OVERTURE 14
Figure 1.9: The 3-disk game of pinball Poincaresection,
trajectories emanating from the disk 1with x0 = (s0, p0) . (a)
Strips of initial pointsM12,M13 which reach disks 2, 3 in one
bounce, respec-tively. (b) Strips of initial pointsM121,M131
M132andM123 which reach disks 1, 2, 3 in two bounces,respectively.
The Poincare sections for trajectoriesoriginating on the other two
disks are obtained bythe appropriate relabeling of the strips. Disk
ra-dius : center separation ratio a:R = 1:2.5. (Y.Lan)
(a)
sin
1
0
12.5
S0 2.5
1312
(b)
1
0
sin
1
2.50s
2.5
132
131123
121
parallel to the billiard wall at the point of impact, see
figure1.9. Such section of aflow is called a Poincare section. In
terms of Poincare sections, the dynamics is
example 15.9reduced to the set of six maps Psks j : (sn, pn)
(sn+1, pn+1), with s {1, 2, 3},from the boundary of the disk j to
the boundary of the next disk k.
chapter 9
Next, we mark in the Poincare section those initial conditions
which do notescape in one bounce. There are two strips of
survivors, as the trajectories orig-inating from one disk can hit
either of the other two disks, or escape withoutfurther ado. We
label the two strips M12, M13. Embedded within them thereare four
stripsM121,M123,M131,M132 of initial conditions that survive for
twobounces, and so forth, see figures 1.8 and 1.9. Provided that
the disks are su-ciently separated, after n bounces the survivors
are divided into 2n distinct strips:the Mith strip consists of all
points with itinerary i = s1s2s3 . . . sn, s = {1, 2, 3}.The
unstable cycles as a skeleton of chaos are almost visible here:
each such patchcontains a periodic point s1s2s3 . . . sn with the
basic block infinitely repeated. Pe-riodic points are skeletal in
the sense that as we look further and further, the stripsshrink but
the periodic points stay put forever.
We see now why it pays to utilize a symbolic dynamics; it
provides a naviga-tion chart through chaotic state space. There
exists a unique trajectory for everyadmissible infinite length
itinerary, and a unique itinerary labels every trappedtrajectory.
For example, the only trajectory labeled by 12 is the 2-cycle
bouncingalong the line connecting the centers of disks 1 and 2; any
other trajectory startingout as 12 . . . either eventually escapes
or hits the 3rd disk.
1.4.3 Escape rateexample 20.4
What is a good physical quantity to compute for the game of
pinball? Such a sys-tem, for which almost any trajectory eventually
leaves a finite region (the pinballtable) never to return, is said
to be open, or a repeller. The repeller escape rateis an eminently
measurable quantity. An example of such a measurement wouldbe an
unstable molecular or nuclear state which can be well approximated
by aclassical potential with the possibility of escape in certain
directions. In an ex-periment many projectiles are injected into a
macroscopic black box enclosinga microscopic non-confining
short-range potential, and their mean escape rate ismeasured, as in
figure 1.1. The numerical experiment might consist of injecting
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CHAPTER 1. OVERTURE 15
the pinball between the disks in some random direction and
asking how manytimes the pinball bounces on the average before it
escapes the region between thedisks.
exercise 1.2
For a theorist, a good game of pinball consists in predicting
accurately theasymptotic lifetime (or the escape rate) of the
pinball. We now show how periodicorbit theory accomplishes this for
us. Each step will be so simple that you canfollow even at the
cursory pace of this overview, and still the result is
surprisinglyelegant.
Consider figure 1.9 again. In each bounce the initial conditions
get thinnedout, yielding twice as many thin strips as at the
previous bounce. The total areathat remains at a given time is the
sum of the areas of the strips, so that the fractionof survivors
after n bounces, or the survival probability is given by
1 =|M0||M| +
|M1||M| ,
2 =|M00||M| +
|M10||M| +
|M01||M| +
|M11||M| ,
n =1|M|
(n)i|Mi| , (1.2)
where i is a label of the ith strip, |M| is the initial area,
and |Mi| is the area ofthe ith strip of survivors. i = 01, 10, 11,
. . . is a label, not a binary number. Sinceat each bounce one
routinely loses about the same fraction of trajectories, oneexpects
the sum (1.2) to fall o exponentially with n and tend to the
limit
chapter 27
n+1/ n = en e. (1.3)
The quantity is called the escape rate from the repeller.
1.5 Chaos for cyclists
Etant donnees des equations ... et une solution
particulierequelconque de ces equations, on peut toujours trouver
unesolution periodique (dont la periode peut, il est vrai, etretres
longue), telle que la dierence entre les deux solu-tions soit aussi
petite quon le veut, pendant un temps aussilong quon le veut.
Dailleurs, ce qui nous rend ces solu-tions periodiques si
precieuses, cest quelles sont, pouransi dire, la seule breche par
ou` nous puissions esseyer depenetrer dans une place jusquici
reputee inabordable.
H. Poincare, Les methodes nouvelles de lamechanique celeste
We shall now show that the escape rate can be extracted from a
highly conver-gent exact expansion by reformulating the sum (1.2)
in terms of unstable periodicorbits.
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CHAPTER 1. OVERTURE 16
If, when asked what the 3-disk escape rate is for a disk of
radius 1, center-center separation 6, velocity 1, you answer that
the continuous time escape rateis roughly =
0.4103384077693464893384613078192 . . ., you do not need thisbook.
If you have no clue, hang on.
1.5.1 How big is my neighborhood?
Of course, we can prove all these results directly fromEq.
(20.25) by pedestrian mathematical manipulations,but that only
makes it harder to appreciate their physicalsignificance.
Rick Salmon, Lectures on Geophysical Fluid Dy-namics, Oxford
Univ. Press (1998)
Not only do the periodic points keep track of topological
ordering of the strips,but, as we shall now show, they also
determine their size. As a trajectory evolves,it carries along and
distorts its infinitesimal neighborhood. Let
x(t) = f t(x0)
denote the trajectory of an initial point x0 = x(0). Expanding f
t(x0 + x0) tolinear order, the evolution of the distance to a
neighboring trajectory x(t) + x(t)is given by the Jacobian matrix
J:
xi(t) =d
j=1Jt(x0)i jx0 j , Jt(x0)i j =
xi(t)x0 j
. (1.4)
A trajectory of a pinball moving on a flat surface is specified
by two positioncoordinates and the direction of motion, so in this
case d = 3. Evaluation ofa cycle Jacobian matrix is a long exercise
- here we just state the result. The
section 9.2Jacobian matrix describes the deformation of an
infinitesimal neighborhood ofx(t) along the flow; its eigenvectors
and eigenvalues give the directions and thecorresponding rates of
expansion or contraction, figure1.10. The trajectories thatstart
out in an infinitesimal neighborhood separate along the unstable
directions(those whose eigenvalues are greater than unity in
magnitude), approach eachother along the stable directions (those
whose eigenvalues are less than unity inmagnitude), and change
their distance only sub-exponentially (or not at all) alongthe
marginal directions (those whose eigenvalues equal unity in
magnitude).
In our game of pinball the beam of neighboring trajectories is
defocused alongthe unstable eigen-direction of the Jacobian matrix
J.
As the heights of the strips in figure 1.9 are eectively
constant, we can con-centrate on their thickness. If the height is
L, then the area of the ith strip isMi Lli for a strip of width
li.
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CHAPTER 1. OVERTURE 17
Figure 1.10: The Jacobian matrix Jt maps an infinites-imal
displacement x at x0 into a displacement Jt(x0)xa finite time t
later.
x(t) = J t x(0)
x(0)x(0)
x(t)
Each strip i in figure 1.9 contains a periodic point xi. The
finer the intervals,the smaller the variation in flow across them,
so the contribution from the stripof width li is well-approximated
by the contraction around the periodic point xiwithin the
interval,
li = ai/|i| , (1.5)
where i is the unstable eigenvalue of the Jacobian matrix Jt(xi)
evaluated atthe ith periodic point for t = Tp, the full period (due
to the low dimensionality,the Jacobian can have at most one
unstable eigenvalue). Only the magnitude ofthis eigenvalue matters,
we can disregard its sign. The prefactors ai reflect theoverall
size of the system and the particular distribution of starting
values of x. Asthe asymptotic trajectories are strongly mixed by
bouncing chaotically around therepeller, we expect their
distribution to be insensitive to smooth variations in
thedistribution of initial points.
section 19.4
To proceed with the derivation we need the hyperbolicity
assumption: forlarge n the prefactors ai O(1) are overwhelmed by
the exponential growth ofi, so we neglect them. If the
hyperbolicity assumption is justified, we can replace section
21.1.1|Mi| Lli in (1.2) by 1/|i| and consider the sum
n =
(n)i
1/|i| ,
where the sum goes over all periodic points of period n. We now
define a gener-ating function for sums over all periodic orbits of
all lengths:
(z) =
n=1nz
n . (1.6)
Recall that for large n the nth level sum (1.2) tends to the
limit n en, so theescape rate is determined by the smallest z = e
for which (1.6) diverges:
(z)
n=1(ze)n = ze
1 ze . (1.7)
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CHAPTER 1. OVERTURE 18
This is the property of (z) that motivated its definition. Next,
we devise a formulafor (1.6) expressing the escape rate in terms of
periodic orbits:
(z) =
n=1zn
(n)i|i|1
=z
|0| +z
|1| +z2
|00| +z2
|01| +z2
|10| +z2
|11|+
z3
|000| +z3
|001| +z3
|010| +z3
|100| + . . . (1.8)
For suciently small z this sum is convergent. The escape rate is
now given bysection 21.3
the leading pole of (1.7), rather than by a numerical
extrapolation of a sequence ofn extracted from (1.3). As any finite
truncation n < ntrunc of (1.8) is a polyno-mial in z, convergent
for any z, finding this pole requires that we know somethingabout n
for any n, and that might be a tall order.
We could now proceed to estimate the location of the leading
singularity of(z) from finite truncations of (1.8) by methods such
as Pade approximants. How-ever, as we shall now show, it pays to
first perform a simple resummation thatconverts this divergence
into a zero of a related function.
1.5.2 Dynamical zeta function
If a trajectory retraces a prime cycle r times, its expanding
eigenvalue is rp. Aprime cycle p is a single traversal of the
orbit; its label is a non-repeating symbolstring of np symbols.
There is only one prime cycle for each cyclic permutationclass. For
example, p = 0011 = 1001 = 1100 = 0110 is prime, but 0101 = 01 is
not.By the chain rule for derivatives the stability of a cycle is
the same everywhere
exercise 18.2section 4.5along the orbit, so each prime cycle of
length np contributes np terms to the sum
(1.8). Hence (1.8) can be rewritten as
(z) =
pnp
r=1
(znp
|p|)r=
p
nptp1 tp , tp =
znp
|p| (1.9)
where the index p runs through all distinct prime cycles. Note
that we have re-summed the contribution of the cycle p to all
times, so truncating the summationup to given p is not a finite
time n np approximation, but an asymptotic, infinitetime estimate
based by approximating stabilities of all cycles by a finite number
ofthe shortest cycles and their repeats. The npznp factors in (1.9)
suggest rewritingthe sum as a derivative
(z) = z ddz
pln(1 tp) .
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CHAPTER 1. OVERTURE 19
Hence (z) is z derivative derivative of the logarithm of the
infinite product
1/(z) =
p(1 tp) , tp = z
np
|p| . (1.10)
This function is called the dynamical zeta function, in analogy
to the Riemannzeta function, which motivates the zeta in its
definition as 1/(z). This is theprototype formula of periodic orbit
theory. The zero of 1/(z) is a pole of (z),and the problem of
estimating the asymptotic escape rates from finite n sumssuch as
(1.2) is now reduced to a study of the zeros of the dynamical zeta
function(1.10). The escape rate is related by (1.7) to a divergence
of (z), and (z) diverges
section 27.1whenever 1/(z) has a zero.
section 22.4
Easy, you say: Zeros of (1.10) can be read o the formula, a
zero
zp = |p|1/np
for each term in the product. Whats the problem? Dead wrong!
1.5.3 Cycle expansions
How are formulas such as (1.10) used? We start by computing the
lengths andeigenvalues of the shortest cycles. This usually
requires some numerical work,such as the Newton method searches for
periodic solutions; we shall assume thatthe numerics are under
control, and that all short cycles up to given length havebeen
found. In our pinball example this can be done by elementary
geometrical
chapter 16optics. It is very important not to miss any short
cycles, as the calculation is asaccurate as the shortest cycle
droppedincluding cycles longer than the shortestomitted does not
improve the accuracy. The result of such numerics is a table ofthe
shortest cycles, their periods and their stabilities.
section 33.3
Now expand the infinite product (1.10), grouping together the
terms of thesame total symbol string length
1/ = (1 t0)(1 t1)(1 t10)(1 t100) = 1 t0 t1 [t10 t1t0] [(t100
t10t0) + (t101 t10t1)][(t1000 t0t100) + (t1110 t1t110)+(t1001
t1t001 t101t0 + t10t0t1)] . . . (1.11)
The virtue of the expansion is that the sum of all terms of the
same total lengthchapter 23
n (grouped in brackets above) is a number that is exponentially
smaller than atypical term in the sum, for geometrical reasons we
explain in the next section.
section 23.1
The calculation is now straightforward. We substitute a finite
set of the eigen-values and lengths of the shortest prime cycles
into the cycle expansion (1.11), andobtain a polynomial
approximation to 1/. We then vary z in (1.10) and determinethe
escape rate by finding the smallest z = e for which (1.11)
vanishes.
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CHAPTER 1. OVERTURE 20
Figure 1.11: Approximation to a smooth dynamics(left frame) by
the skeleton of periodic points, togetherwith their linearized
neighborhoods, (right frame). In-dicated are segments of two
1-cycles and a 2-cyclethat alternates between the neighborhoods of
the two1-cycles, shadowing first one of the two 1-cycles, andthen
the other.
Figure 1.12: A longer cycle p shadowed by a pair (apseudo orbit)
of shorter cycles p and p.
p
p"p
1.5.4 Shadowing
When you actually start computing this escape rate, you will
find out that theconvergence is very impressive: only three input
numbers (the two fixed points 0,1 and the 2-cycle 10) already yield
the pinball escape rate to 3-4 significant digits!We have omitted
an infinity of unstable cycles; so why does approximating the
section 23.2.2dynamics by a finite number of the shortest cycle
eigenvalues work so well?
The convergence of cycle expansions of dynamical zeta functions
is a conse-quence of the smoothness and analyticity of the
underlying flow. Intuitively, onecan understand the convergence in
terms of the geometrical picture sketched infigure 1.11; the key
observation is that the long orbits are shadowed by sequencesof
shorter orbits.
A typical term in (1.11) is a dierence of a long cycle {ab}minus
its shadowingapproximation by shorter cycles {a} and {b} (see
figure1.12),
tab tatb = tab(1 tatb/tab) = tab(1
abab) , (1.12)
where a and b are symbol sequences of the two shorter cycles. If
all orbits areweighted equally (tp = znp ), such combinations
cancel exactly; if orbits of similarsymbolic dynamics have similar
weights, the weights in such combinations almostcancel.
This can be understood in the context of the pinball game as
follows. Considerorbits 0, 1 and 01. The first corresponds to
bouncing between any two disks whilethe second corresponds to
bouncing successively around all three, tracing out anequilateral
triangle. The cycle 01 starts at one disk, say disk 2. It then
bounces
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CHAPTER 1. OVERTURE 21
from disk 3 back to disk 2 then bounces from disk 1 back to disk
2 and so on, so itsitinerary is 2321. In terms of the bounce types
shown in figure1.6, the trajectory isalternating between 0 and 1.
The incoming and outgoing angles when it executesthese bounces are
very close to the corresponding angles for 0 and 1 cycles. Alsothe
distances traversed between bounces are similar so that the 2-cycle
expandingeigenvalue 01 is close in magnitude to the product of the
1-cycle eigenvalues01.
To understand this on a more general level, try to visualize the
partition ofa chaotic dynamical systems state space in terms of
cycle neighborhoods as atessellation (a tiling) of the dynamical
system, with smooth flow approximated byits periodic orbit
skeleton, each tile centered on a periodic point, and the scaleof
the tile determined by the linearization of the flow around the
periodic point,as illustrated by figure 1.11.
The orbits that follow the same symbolic dynamics, such as {ab}
and a pseudoorbit {a}{b} (see figure 1.12), lie close to each other
in state space; long shadow-ing pairs have to start out
exponentially close to beat the exponential growth inseparation
with time. If the weights associated with the orbits are
multiplicativealong the flow (for example, by the chain rule for
products of derivatives) andthe flow is smooth, the term in
parenthesis in (1.12) falls o exponentially withthe cycle length,
and therefore the curvature expansions are expected to be
highlyconvergent.
chapter 28
1.6 Change in time
MEN are deplorably ignorant with respect to naturalthings and
modern philosophers as though dreaming in thedarkness must be
aroused and taught the uses of things thedealing with things they
must be made to quit the sort oflearning that comes only from books
and that rests onlyon vain arguments from probability and upon
conjectures.
William Gilbert, De Magnete, 1600
The above derivation of the dynamical zeta function formula for
the escape ratehas one shortcoming; it estimates the fraction of
survivors as a function of thenumber of pinball bounces, but the
physically interesting quantity is the escaperate measured in units
of continuous time. For continuous time flows, the escaperate (1.2)
is generalized as follows. Define a finite state space region M
suchthat a trajectory that exits M never reenters. For example, any
pinball that fallsof the edge of a pinball table in figure 1.1 is
gone forever. Start with a uniformdistribution of initial points.
The fraction of initial x whose trajectories remainwithinM at time
t is expected to decay exponentially
(t) =M dxdy (y f t(x))
M dx et .
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CHAPTER 1. OVERTURE 22
The integral over x starts a trajectory at every x M. The
integral over y testswhether this trajectory is still inM at time
t. The kernel of this integral
Lt(y, x) = (y f t(x)
)(1.13)
is the Dirac delta function, as for a deterministic flow the
initial point x mapsinto a unique point y at time t. For discrete
time, fn(x) is the nth iterate of themap f . For continuous flows,
f t(x) is the trajectory of the initial point x, and itis
appropriate to express the finite time kernel Lt in terms of A, the
generator ofinfinitesimal time translations
Lt = etA ,section 19.6
very much in the way the quantum evolution is generated by the
Hamiltonian H,the generator of infinitesimal time quantum
transformations.
As the kernel L is the key to everything that follows, we shall
give it a name,and refer to it and its generalizations as the
evolution operator for a d-dimensionalmap or a d-dimensional
flow.
The number of periodic points increases exponentially with the
cycle length(in the case at hand, as 2n). As we have already seen,
this exponential proliferationof cycles is not as dangerous as it
might seem; as a matter of fact, all our compu-tations will be
carried out in the n limit. T