math matters, ima march 7, 2007 · math matters, ima march 7, 2007 martin golubitsky department of...
TRANSCRIPT
Patterns Patterns Everywhere
Math Matters, IMAMarch 7, 2007
Martin GolubitskyDepartment of Mathematics
University of Houston
– p. 1/64
Why Study Patterns I
Patterns are surprising and pretty
Thanks to Stephen Morris and Michael Gorman
– p. 2/64
Mud Plains
– p. 3/64
Leopard Spots
– p. 4/64
Convection Cells
Bodenschatz, de Bruyn, Cannell, and Ahlers, 1991– p. 5/64
Sand Dunes in Namibian Desert
– p. 6/64
Zebra Stripes
– p. 7/64
Convection Cells
– p. 8/64
Porous Plug Burner Flames (Gorman)
burner
Air & fuel Inert gas
Flame front
Cooling coils
Dynamic patterns
A film in three parts
stationary patternstime-periodic patternsmore complicatedspatio-temporal patterns
– p. 9/64
Porous Plug Burner Flames (Gorman)
burner
Air & fuel Inert gas
Flame front
Cooling coils
Dynamic patterns
A film in three parts
stationary patternstime-periodic patternsmore complicatedspatio-temporal patterns
– p. 9/64
Why Study Patterns II1) Patterns are surprising and pretty
2) Emergent phenomena; self organization
Patterns often provide functionScience behind patterns
– p. 10/64
Patterns We Use
– p. 11/64
Patterns We Use
– p. 12/64
Columnar Joints on Staffa near Mull
– p. 13/64
Columns along Snake River
– p. 14/64
Experiment on Corn Starch
Goehring and Morris, 2005
– p. 15/64
Why Study Patterns III1) Patterns are surprising and pretty
2) Emergent phenomena; self organizationPatterns provide functionScience behind patterns
3) Change in patterns provide tests for models
– p. 16/64
A Brief History of Navier-StokesNavier-Stokes equations for an incompressible fluid
ut = ν∇2u − (u · ∇)u −1
ρ∇p
0 = ∇ · u
u = velocity vector ρ = mass densityp = pressure ν = kinematic viscosity
Navier (1821); Stokes (1856); Taylor (1923)
– p. 17/64
The Couette Taylor Experiment
Ω Ωo i
Ωi = speed of innercylinderΩo = speed of outercylinder
Andereck, Liu, and Swinney (1986)
Couette Taylor Spiraltime independent time periodic
– p. 18/64
G.I. Taylor: Theory & Experiment (1923)
– p. 19/64
Why Study Patterns IV1) Patterns are surprising and pretty2) Emergent phenomena; self organization
Patterns provide functionScience behind patterns
3) Change in patterns provide tests for models
4) Model independenceMathematics provides menu of patternsTwo Examples
Stripes and Spots in planar systemsOscillations with circle symmetry
– p. 20/64
Planar Symmetry-BreakingEuclidean symmetry: translations, rotations, reflections
Symmetry-breaking from translation invariant state inplanar systems with Euclidean symmetry leads to
stripes or spots
Stripes: invariant under translation in one directionSand dunes, zebra, convection cells
Spots: states centered at lattice pointsmud plains, leopard, convection cells
– p. 21/64
Planar Symmetry-BreakingEuclidean symmetry: translations, rotations, reflections
Symmetry-breaking from translation invariant state inplanar systems with Euclidean symmetry leads to
stripes or spots
Stripes: invariant under translation in one directionSand dunes, zebra, convection cells
Spots: states centered at lattice pointsmud plains, leopard, convection cells
– p. 21/64
Circle Symmetry-Breaking Oscillation
There exist two types of time-periodic solutionsnear a circularly symmetric equilibrium
Rotating waves:Time evolution is the same as spatial rotationStanding waves:Fixed line of symmetry for all time
– p. 22/64
Burner with Circular Symmetry
Porous plug burner (Gorman)
Parameters:flow rate and stochiometry
burner
Air & fuel Inert gas
Flame front
Cooling coils
Rotating wave time evolution = spatial rotation
Standing wave line of symmetry
– p. 23/64
Taylor-Couette ExperimentThe states: Andereck, Liu, and Swinney (1986)
vortexTaylor
flow
Wavyoutflow
Wavy inflow+ twists
Ripple
Corkscrewwavelets
Twists
Couette flow
Wavy vortices
Wavy inflow
Modulated waves
Turbulent Taylor vortices
Spiral turbulence
-4000 -3000 -2000 -1000 0 1000
0
2000
1000
Featureless turbulenceUnexplored
Spirals
Wavy spirals
Interpenetrating spirals
Couette flow
Intermittency Wavyvortexflow
Ro
Ri
Modulated
wav
es
1
Prediction of Ribbons: Chossat, Iooss, Demay (1987)
– p. 24/64
Taylor-Couette ExperimentThe states: Andereck, Liu, and Swinney (1986)
Prediction of Ribbons: Chossat, Iooss, Demay (1987)
– p. 24/64
Summary on Pattern Formation
The mathematics of pattern formation leads to amenu of patterns
This menu is model independent
Physics and biology choose from the menu of pattens
This choice is model dependent
– p. 25/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
f(x, y) = (y − x3,−x − xy − y3)
z0 = (.25, .26) z1 = (.2444,−.3326) z2 = (.3472,−.1263)
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
−0.6 −0.4 −0.2 0 0.2 0.4 0.6−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
0
1
2
3
4
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
−0.6 −0.4 −0.2 0 0.2 0.4 0.6−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
0
1
2
3
4
5
6
7
8
9
10
11
1213
14
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
−0.6 −0.4 −0.2 0 0.2 0.4 0.6−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Planar Dynamics; Symmetric ChaosLet f : R2
→ R2 be a function
Choose a point z0 in the plane
Let z1 = f(z0), z2 = f(z1), . . .
The trajectory z0, z1, z2, . . . is chaotic if it hassensitive dependence on initial conditions
To be explained by pictures — please wait
– p. 26/64
Finite Symmetries on the PlaneD3 = symmetries of an equilateral triangleD4 = symmetries of a squareD5 = symmetries of a regular pentagon
Dm consists of rotations and reflections
A planar map has symmetries Dm if
f(gz) = gf(z)
for every symmetry g in Dm
Example of map with Dm-symmetry is
f(z) = (` + azz + bRe(zm))z + czm−1
– p. 27/64
Finite Symmetries on the PlaneD3 = symmetries of an equilateral triangleD4 = symmetries of a squareD5 = symmetries of a regular pentagon
Four Rotations Four Reflections
Dm consists of rotations and reflections
A planar map has symmetries Dm if
f(gz) = gf(z)
for every symmetry g in Dm
Example of map with Dm-symmetry is
f(z) = (` + azz + bRe(zm))z + czm−1
– p. 27/64
Finite Symmetries on the PlaneD3 = symmetries of an equilateral triangleD4 = symmetries of a squareD5 = symmetries of a regular pentagon
Dm consists of rotations and reflections
A planar map has symmetries Dm if
f(gz) = gf(z)
for every symmetry g in Dm
Example of map with Dm-symmetry is
f(z) = (` + azz + bRe(zm))z + czm−1
– p. 27/64
Symmetric Chaos
m=3 m=5
– p. 28/64
IMA Attractor (Mike Field)
– p. 29/64
Emperor’s Cloak
– p. 30/64
Wild Chaos
– p. 31/64
Sensitive Dependence: Golden Flintstone
– p. 32/64
Sensitive Dependence (1 iterate)
– p. 33/64
Sensitive Dependence (3 iterates)
– p. 34/64
Sensitive Dependence (9 iterates)
– p. 35/64
Sensitive Dependence (29 iterates)
– p. 36/64
St. John’s Wort Attractor
– p. 37/64
Mercedes Attractor
– p. 38/64
Pentagon Attractor
– p. 39/64
HexNuts Attractor
– p. 40/64
Mosaic Attractor
– p. 41/64
Consequence of Attractor SymmetryLet u(x, t) be a time series
Ergodic Theorem: Time Average = Space Average
Time average has same symmetries as attractor
Let U(x) = average of u(x, t) over t
Then U(σx) = U(x) for every symmetry σ
– p. 42/64
Faraday Surface Wave ExperimentVibrate a fluid layer at fixed frequency and amplitude
At small amplitude — surface is flat
At large amplitudes — surface deforms
Take picture at each period of forcing
Light is transmitted through the fluidDark areas: surface is concave upBright areas: surface is concave down
– p. 43/64
The Faraday Experiment (2)
Gollub, Gluckman, Marcq, & Bridger (1993)
– p. 44/64
The Faraday Experiment (3)
Gollub, Gluckman, Marcq, & Bridger (1993)
– p. 45/64
The Faraday Experiment (4)
Gollub, Gluckman, Marcq, & Bridger (1993)
– p. 46/64
Patterns in Neuroscience
Animal GaitsStewart, Buono, Collins
Geometric Visual HallucinationsErmentrout, Cowan, Bressloff, Thomas, Wiener
– p. 47/64
Quadruped GaitsBound of the Siberian Souslik
Amble of the Elephant
Pace of the Horse
– p. 48/64
Quadruped GaitsBound of the Siberian Souslik
Amble of the Elephant
Pace of the Horse
– p. 48/64
Quadruped GaitsBound of the Siberian Souslik
Amble of the Elephant
Pace of the Horse
– p. 48/64
Standard Gait Phases
0
0 1/2
1/20
1/4 3/4
0
0
1/2
1/2
0 0
1/2 1/2
0
1/2
0.1
0.6
0
1/2
0 0
0 0
WALK PACE TROT BOUND
TRANSVERSEGALLOP
ROTARYGALLOP
PRONK0.1
0.6
1/2
– p. 49/64
The Pronk
– p. 50/64
Gait SymmetriesGait Spatio-temporal symmetriesTrot (Left/Right, 1
2) and (Front/Back, 1
2)
Pace (Left/Right, 1
2) and (Front/Back, 0)
Walk (Figure Eight, 1
4)
0 1/2 1
PACE:
0 1
TROT:
1/2
WALK: 1/4 1/2 3/4 1 0
Three gaits are different Collins and Stewart (1993)
– p. 51/64
Central Pattern Generators (CPG)Assumption: There is a network in the nervous system thatproduces the characteristic rhythms of each gait
Design simplest network to produce walk, trot, and pace
Guess at simplest network
One unit ‘signals’ each leg1 2
43
No four-unit network can produce independent gaits ofwalk, trot and pace
– p. 52/64
Central Pattern Generators (CPG)Assumption: There is a network in the nervous system thatproduces the characteristic rhythms of each gait
Design simplest network to produce walk, trot, and pace
Guess at simplest network
One unit ‘signals’ each leg1 2
43
No four-unit network can produce independent gaits ofwalk, trot and pace
– p. 52/64
Simplest Coupled Unit Gait ModelUse gait symmetries to construct coupled network1) walk =⇒ four-cycle ω in symmetry group2) pace or trot =⇒ transposition κ in symmetry group3) Simplest network
LF
LH RH
RF
LH
LF RF
RH
1 2
3 4
5 6
7 8
– p. 53/64
Primary Gaits: Oscillations from Stand
Phase Diagram Gait0
@
0 0
0 0
1
A pronk0
@
01
2
01
2
1
A pace0
@
1
20
01
2
1
A trot0
@
0 0
1
2
1
2
1
A bound0
@
±1
4±
3
4
01
2
1
A walk±
0
@
0 0
±1
4±
1
4
1
A jump±
– p. 54/64
The Jump
Average Right Rear to Right Front = 31.2 frames
Average Right Front to Right Rear = 11.4 frames
31.211.4
= 2.74
– p. 55/64
Biped Network
LF
LH RH
RF
LH
LF RF
RH
1 2
3 4
5 6
7 8
3 4
1 2
left right
– p. 56/64
Biped Prediction
0 0.5 0.5 0
left right
0 0.5
left right
0 0.5
(b)(a)
– p. 57/64
Biped Gaits: Walk and Run
0 0.5 0.5 0
left right
0 0.5
left right
0 0.5
(b)(a)
Units control timing of muscle groups
Electromyographic signals from ankle muscles
During walking: gastrocnemius (GA) andtibialis anterior (TA) are activated out-of-phase
During running: (GA) and (TA) are co-activated
– p. 58/64
Geometric Visual Hallucinations
Patterns fall into four form constants (Klüver, 1928)
tunnels and funnels
spirals
lattices includes honeycombs and phosphenes
cobwebs
– p. 59/64
Klüver Form Constants
funnel spiral
honeycomb cobweb– p. 60/64
Cowan-Ermentrout-Bressloff TheoryDrug uniformly forces activation of cortical cells
Leads to spontaneous pattern formation on cortex
Map from retina to primary visual cortex;translates pattern on cortex to visual image
– p. 61/64
Planforms in Visual Field
– p. 62/64
ConclusionsPatterns that appear in physical and biological systemsoften have their genesis in symmetryanimal stripes, convection cells, geometric visualhallucinations, etc.
Using symmetry patterns in systems with differentphysics can have the same mathematical descriptionhose swinging in a circle, rotating flame front,spiral vortices in Taylor-Couette
Complicated patterns can be built in stages by relativelysimple mechanisms—once the mathematical structureof the system is identifiedsymmetric chaos, patterns in animal gaits
– p. 63/64
ConclusionsPatterns that appear in physical and biological systemsoften have their genesis in symmetryanimal stripes, convection cells, geometric visualhallucinations, etc.
Using symmetry patterns in systems with differentphysics can have the same mathematical descriptionhose swinging in a circle, rotating flame front,spiral vortices in Taylor-Couette
Complicated patterns can be built in stages by relativelysimple mechanisms—once the mathematical structureof the system is identifiedsymmetric chaos, patterns in animal gaits
– p. 63/64
ConclusionsPatterns that appear in physical and biological systemsoften have their genesis in symmetryanimal stripes, convection cells, geometric visualhallucinations, etc.
Using symmetry patterns in systems with differentphysics can have the same mathematical descriptionhose swinging in a circle, rotating flame front,spiral vortices in Taylor-Couette
Complicated patterns can be built in stages by relativelysimple mechanisms—once the mathematical structureof the system is identifiedsymmetric chaos, patterns in animal gaits
– p. 63/64
Thank You
– p. 64/64