Simple Models of Complex Chaotic Systems

J. C. SprottDepartment of Physics

University of Wisconsin - Madison

Presented at the

AAPT Topical Conference on Computational Physics in Upper Level Courses

At Davidson College (NC)

On July 28, 2007

Collaborators

David Albers, Univ California - Davis

Konstantinos Chlouverakis, Univ Athens (Greece)

Background Grew out of an multi-disciplinary

chaos course that I taught 3 times

Demands computation

Strongly motivates students

Used now for physics undergraduate research projects (~20 over the past 10 years)

Minimal Chaotic Systems 1-D map (quadratic map)

Dissipative map (Hénon)

Autonomous ODE (jerk equation)

Driven ODE (Ueda oscillator)

Delay differential equation (DDE)

Partial diff eqn (Kuramoto-Sivashinsky)

21 1 nn xx

12

1 1 nnn bxaxx

02 xxxax

txx sin3

3 tt xxx

042 uuauuu xxxt

What is a complex system? Complex ≠ complicated Not real and imaginary parts Not very well defined Contains many interacting parts Interactions are nonlinear Contains feedback loops (+ and -) Cause and effect intermingled Driven out of equilibrium Evolves in time (not static) Usually chaotic (perhaps weakly) Can self-organize and adapt

A Physicist’s Neuron

jN

j jxax 1

tanhoutN

inputs

tanh x

x

2 4

1

3

A General Model (artificial neural network)

N neurons

N

ijj

jijiii xaxbx1

tanh

“Universal approximator,” N ∞

Route to Chaos at Large N (=101)

jj ijii xabxdtdx

101

1tanh/

“Quasi-periodic route to chaos”

Strange Attractors

Sparse Circulant Network (N=101)

jij jii xabxdtdx 9

1tanh/

Labyrinth Chaosx1 x3

x2

dx1/dt = sin x2

dx2/dt = sin x3

dx3/dt = sin x1

Hyperlabyrinth Chaos (N=101)

1sin/ iii xbxdtdx

Minimal High-D Chaotic L-V Modeldxi /dt = xi(1 – xi – 2 – xi – xi+1)

Lotka-Volterra Model (N=101))1(/ 12 iiiii xbxxxdtdx

Delay Differential Equation

txdtdx sin/

Partial Differential Equation

02/ 42 buuuuuu xxxt

Summary of High-N Dynamics Chaos is common for highly-connected networks

Sparse, circulant networks can also be chaotic (but

the parameters must be carefully tuned)

Quasiperiodic route to chaos is usual

Symmetry-breaking, self-organization, pattern

formation, and spatio-temporal chaos occur

Maximum attractor dimension is of order N/2

Attractor is sensitive to parameter perturbations,

but dynamics are not

Shameless PlugChaos and Time-Series Analysis

J. C. SprottOxford University Press (2003)

ISBN 0-19-850839-5

An introductory text for advanced undergraduateand beginning graduatestudents in all fields of science and engineering

References

http://sprott.physics.wisc.edu

/ lectures/davidson.ppt (this talk)

http://sprott.physics.wisc.edu/chaostsa/

(my chaos textbook)

sprott@physics.wisc.edu (contact

me)