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Mathematical modeling of self-organized dynamics: from...
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Introduction PTW model Vicsek model Numerical schemes
Mathematical modeling of self-organized dynamics:from microscopic to macroscopic description
Sebastien Motsch
CSCAMM, University of Maryland
joint work with : P. Degond, L. Navoret (IMT, Toulouse)
G. Theraulaz, J. Gautrais (CRCA, Toulouse)
UC Davis, Applied Math PDE Seminar
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 1/ 37
Introduction PTW model Vicsek model Numerical schemes
Outline
1 PTW modelExperiments and modelDerivation of a diffusion equation
2 Vicsek modelThe modelDerivation of a hyperbolic system
3 Numerical schemesSplitting methodParticle simulationsMicro vs macro
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 2/ 37
Introduction PTW model Vicsek model Numerical schemes
Motivation
Individuals have only local interactions
There is no leader inside the group (⇒ self-organization)
The global organization of the group is at a much larger scalethan the individual size
How can we connect individual and global dynamics ?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 3/ 37
Introduction PTW model Vicsek model Numerical schemes
Motivation
Individuals have only local interactions
There is no leader inside the group (⇒ self-organization)
The global organization of the group is at a much larger scalethan the individual size
How can we connect individual and global dynamics ?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 3/ 37
Introduction PTW model Vicsek model Numerical schemes
Motivation
Individuals have only local interactions
There is no leader inside the group (⇒ self-organization)
The global organization of the group is at a much larger scalethan the individual size
How can we connect individual and global dynamics ?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 3/ 37
Introduction PTW model Vicsek model Numerical schemes
Motivation
Individuals have only local interactions
There is no leader inside the group (⇒ self-organization)
The global organization of the group is at a much larger scalethan the individual size
How can we connect individual and global dynamics ?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 3/ 37
Introduction PTW model Vicsek model Numerical schemes
Motivation
Individuals have only local interactions
There is no leader inside the group (⇒ self-organization)
The global organization of the group is at a much larger scalethan the individual size
How can we connect individual and global dynamics ?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 3/ 37
Introduction PTW model Vicsek model Numerical schemes
Methodology
Individual Based
Model (IBM)
Statistical
analysis
Kinetic
equation
Experiments,
data recording
Macroscopic
equation
Identify the rules Capture the globalfor individual behavior behavior of the system
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 4/ 37
Introduction PTW model Vicsek model Numerical schemes
Methodology
Individual Based
Model (IBM)
Statistical
analysis
Kinetic
equation
Experiments,
data recording
Macroscopic
equation
Identify the rules Capture the globalfor individual behavior behavior of the system
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 4/ 37
Introduction PTW model Vicsek model Numerical schemes
Methodology
Individual Based
Model (IBM)
Statistical
analysis
Kinetic
equation
Experiments,
data recording
Macroscopic
equation
Identify the rules Capture the globalfor individual behavior behavior of the system
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 4/ 37
Introduction PTW model Vicsek model Numerical schemes
Outline
1 PTW modelExperiments and modelDerivation of a diffusion equation
2 Vicsek modelThe modelDerivation of a hyperbolic system
3 Numerical schemesSplitting methodParticle simulationsMicro vs macro
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 5/ 37
Introduction PTW model Vicsek model Numerical schemes
Experiments for fish
The diameter of the basin is 4 meters
Species studied: Kuhlia mugil (20-25 cm)
Video , data recorded
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 6/ 37
Introduction PTW model Vicsek model Numerical schemes
An example of trajectory :
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
−1.5
−1
−0.5
0
0.5
1
1.5
The norm of the velocity is constant
The trajectory is smooth, the fish seems to turn constantly
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 7/ 37
Introduction PTW model Vicsek model Numerical schemes
An example of trajectory :
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
−1.5
−1
−0.5
0
0.5
1
1.5
κ1κ2
κ3
κ4
κ5
The norm of the velocity is constant
The trajectory is smooth, the fish seems to turn constantly
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 7/ 37
Introduction PTW model Vicsek model Numerical schemes
PTW model
The model proposed is the following:
d~x
dt= c~τ(θ)
dθ
dt= cκ
dκ = −aκ dt + b dBt
where c is the speed, a the inverse of a relaxation time, b theintensity of diffusion.
We call this model “Persistent Turning Walker” (PTW)1.
1Gautrais et al., J. Math. Biol. (2009)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 8/ 37
Introduction PTW model Vicsek model Numerical schemes
PTW model
The model proposed is the following:
d~x
dt= c~τ(θ)
dθ
dt= cκ
dκ = −aκ dt + b dBt
where c is the speed, a the inverse of a relaxation time, b theintensity of diffusion.
We call this model “Persistent Turning Walker” (PTW)1.
1Gautrais et al., J. Math. Biol. (2009)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 8/ 37
Introduction PTW model Vicsek model Numerical schemes
PTW model
The model proposed is the following:
d~x
dt= c~τ(θ)
dθ
dt= cκ
dκ = −aκ dt + b dBt
where c is the speed, a the inverse of a relaxation time, b theintensity of diffusion.
We call this model “Persistent Turning Walker” (PTW)1.
1Gautrais et al., J. Math. Biol. (2009)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 8/ 37
Introduction PTW model Vicsek model Numerical schemes
PTW model
The model proposed is the following:
d~x
dt= c~τ(θ)
dθ
dt= cκ
dκ = −aκ dt + b dBt
where c is the speed, a the inverse of a relaxation time, b theintensity of diffusion.
We call this model “Persistent Turning Walker” (PTW)1.
1Gautrais et al., J. Math. Biol. (2009)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 8/ 37
Introduction PTW model Vicsek model Numerical schemes
PTW model
The model proposed is the following:
d~x
dt= c~τ(θ)
dθ
dt= cκ
dκ = −aκ dt + b dBt
where c is the speed, a the inverse of a relaxation time, b theintensity of diffusion.
We call this model “Persistent Turning Walker” (PTW)1.
1Gautrais et al., J. Math. Biol. (2009)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 8/ 37
Introduction PTW model Vicsek model Numerical schemes
Comparison Data and Model
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
−1.5
−1
−0.5
0
0.5
1
1.5
Experiment
−2 −1 0 1 2
−1
0
1
Simulation (PTW model)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 9/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a diffusion equation
To analyze the large scale dynamics of the PTW model, it is moreconvenient to manipulate the density distribution of particlesf(t, x, θ, κ).
PTW model Kinetic equation
d~x
dt= ~τ(θ)
dθ
dt= κ
dκ = −κ dt +√
2α dBt
(in scaled variables)
∂t f + ~τ · ∇~x f
with
Lf = −κ∂θf +∂κ(κf )+α2∂κ2f
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 10/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a diffusion equation
To analyze the large scale dynamics of the PTW model, it is moreconvenient to manipulate the density distribution of particlesf(t, x, θ, κ).
PTW model Kinetic equation
d~x
dt= ~τ(θ)
dθ
dt= κ
dκ = −κ dt +√
2α dBt
(in scaled variables)
∂t f + ~τ · ∇~x f
with
Lf = −κ∂θf +∂κ(κf )+α2∂κ2f
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 10/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a diffusion equation
To analyze the large scale dynamics of the PTW model, it is moreconvenient to manipulate the density distribution of particlesf(t, x, θ, κ).
PTW model Kinetic equation
d~x
dt= ~τ(θ)
dθ
dt= κ
dκ = −κ dt +√
2α dBt
(in scaled variables)
∂t f +~τ ·∇~x f +κ∂θf−∂κ(κf ) = α2∂κ2f
with
Lf = −κ∂θf +∂κ(κf )+α2∂κ2f
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 10/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a diffusion equation
To analyze the large scale dynamics of the PTW model, it is moreconvenient to manipulate the density distribution of particlesf(t, x, θ, κ).
PTW model Kinetic equation
d~x
dt= ~τ(θ)
dθ
dt= κ
dκ = −κ dt +√
2α dBt
(in scaled variables)
∂t f + ~τ · ∇~x f = Lf
with
Lf = −κ∂θf +∂κ(κf )+α2∂κ2f
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 10/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a macroscopic model
Step 1. Diffusive scaling: t ′ = ε2t, x ′ = εx .In these macroscopic variables, f ε satisfies:
ε∂t fε + ~τ · ∇x f ε =
1
εL(f ε). (1)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
Lf 0 = 0 ⇒ f 0 = ρ0(x)N (κ)2π (equilibrium)
with N a Gaussian with zero mean and variance α2.
Step 3. Integrate in (θ, κ):∫θ,κ
(ε∂t f
ε + ~τ · ∇x f ε =1
εL(f ε)
)dθdκ.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 11/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a macroscopic model
Step 1. Diffusive scaling: t ′ = ε2t, x ′ = εx .In these macroscopic variables, f ε satisfies:
ε∂t fε + ~τ · ∇x f ε =
1
εL(f ε). (1)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
Lf 0 = 0 ⇒ f 0 = ρ0(x)N (κ)2π (equilibrium)
with N a Gaussian with zero mean and variance α2.
Step 3. Integrate in (θ, κ):∫θ,κ
(ε∂t f
ε + ~τ · ∇x f ε =1
εL(f ε)
)dθdκ.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 11/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a macroscopic model
Step 1. Diffusive scaling: t ′ = ε2t, x ′ = εx .In these macroscopic variables, f ε satisfies:
ε∂t fε + ~τ · ∇x f ε =
1
εL(f ε). (1)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
Lf 0 = 0 ⇒ f 0 = ρ0(x)N (κ)2π (equilibrium)
with N a Gaussian with zero mean and variance α2.
Step 3. Integrate in (θ, κ):∫θ,κ
(ε∂t f
ε + ~τ · ∇x f ε =1
εL(f ε)
)dθdκ.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 11/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a macroscopic model
Step 1. Diffusive scaling: t ′ = ε2t, x ′ = εx .In these macroscopic variables, f ε satisfies:
ε∂t fε + ~τ · ∇x f ε =
1
εL(f ε). (1)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
Lf 0 = 0 ⇒ f 0 = ρ0(x)N (κ)2π (equilibrium)
with N a Gaussian with zero mean and variance α2.
Step 3. Integrate in (θ, κ):∫θ,κ
(ε∂t f
ε + ~τ · ∇x f ε =1
εL(f ε)
)dθdκ.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 11/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a macroscopic model
Step 1. Diffusive scaling: t ′ = ε2t, x ′ = εx .In these macroscopic variables, f ε satisfies:
ε∂t fε + ~τ · ∇x f ε =
1
εL(f ε). (1)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
Lf 0 = 0 ⇒ f 0 = ρ0(x)N (κ)2π (equilibrium)
with N a Gaussian with zero mean and variance α2.
Step 3. Integrate in (θ, κ):∫θ,κ
(ε∂t f
ε + ~τ · ∇x f ε =1
εL(f ε)
)dθdκ.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 11/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
= x0 +
∫0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. ,Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
= x0 +
∫0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. ,Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
Diffusion equation
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
= x0 +
∫0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. ,Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
Diffusion equation
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
x(t) = x0 +
∫ t
0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. , Chafaı, Cattiaux, M., Asymp. An.Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
Diffusion equation
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
xε(t ′) = εx0 + ε
∫ t′/ε2
0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. , Chafaı, Cattiaux, M., Asymp. An.Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Diffusion equation
Thm.2 The distribution f ε solution of (1) satisfies:
f εε→0 ρ0 N (κ)
2π,
with:
Diffusion equation
∂tρ0 +∇~x · J0 = 0,
J0 = −D∇~xρ0,
where D =∫∞
0 exp−α2(−1+s+e−s) ds.
Probabilistic point of view.
xε(t ′) = εx0 + ε
∫ t′/ε2
0cos(θs) ds
ε→0−→ 0 + D Bt′
2Degond, M., J. Stat. Phys. , Chafaı, Cattiaux, M., Asymp. An.Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 12/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-10
-5
0
5
10
-10 -5 0 5 10
y
x
T=20 (epsilon=1)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10 15
y
x
T=40 (epsilon=0.5)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20 30
y
x
T=100 (epsilon=0.2)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-40
-30
-20
-10
0
10
20
30
40
-40 -30 -20 -10 0 10 20 30 40
y
x
T=200 (epsilon=0.1)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-60
-40
-20
0
20
40
60
-60 -40 -20 0 20 40 60
y
x
T=400 (epsilon=0.05)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-100
-50
0
50
100
-100 -50 0 50 100
y
x
T=2000 (epsilon=0.01)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Illustration: one trajectory ~x(t)
-150
-100
-50
0
50
100
150
-150 -100 -50 0 50 100 150
y
x
T=4000 (epsilon=0.005)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 13/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
In group, fish are usually aligned
To measure this effect, we observe the velocity of theneighbors in the frame of reference of one fish :
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 14/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
In group, fish are usually aligned
To measure this effect, we observe the velocity of theneighbors in the frame of reference of one fish :
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 14/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
In group, fish are usually aligned
To measure this effect, we observe the velocity of theneighbors in the frame of reference of one fish :
P1
P2
~v1
~v2
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 14/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
In group, fish are usually aligned
To measure this effect, we observe the velocity of theneighbors in the frame of reference of one fish :
P1
P2
~v1
~v2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Experimental data
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 14/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
In group, fish are usually aligned
To measure this effect, we observe the velocity of theneighbors in the frame of reference of one fish :
P1
P2
~v1
~v2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Experimental data
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 14/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
Classical model with 3 zones
: attraction
: alignment
: repulsive
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 15/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
Classical model with 3 zones
: attraction
: alignment
: repulsive
Ref.: Aoki (1982), Reynolds (1986),
Huth-Wissel (1992), Couzin et al. (2002),...
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 15/ 37
Introduction PTW model Vicsek model Numerical schemes
Fish in interaction
Classical model with 3 zones
: alignment
Ref.: Vicsek (1995), Gregoire-Chate (2004),
Cucker-Smale (2007), Ha-Tadmor (2008),...
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 15/ 37
Introduction PTW model Vicsek model Numerical schemes
Outline
1 PTW modelExperiments and modelDerivation of a diffusion equation
2 Vicsek modelThe modelDerivation of a hyperbolic system
3 Numerical schemesSplitting methodParticle simulationsMicro vs macro
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 16/ 37
Introduction PTW model Vicsek model Numerical schemes
Vicsek model (’95)
Discrete dynamics:
RΩi
xiωi
xn+1i = xn
i +∆t ωni
ωn+1i = Ω
ni + ε
(2)
with Ωni =
∑|xj−xi |<R ωn
j∣∣∣∑|xj−xi |<R ωnj
∣∣∣ , ε noise.
Continuous dynamics:
dxidt = ωi
dωi = (Id− ωi ⊗ ωi )( ν Ωi dt +√
2D dBt)(3)
Remark. eq. (3) + “ν∆t = 1” ⇒ eq. (2)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 17/ 37
Introduction PTW model Vicsek model Numerical schemes
Vicsek model (’95)
Discrete dynamics:
RΩi
xiωi
xn+1i = xn
i +∆t ωni
ωn+1i = Ω
ni + ε
(2)
with Ωni =
∑|xj−xi |<R ωn
j∣∣∣∑|xj−xi |<R ωnj
∣∣∣ , ε noise.
Continuous dynamics:
dxidt = ωi
dωi = (Id− ωi ⊗ ωi )( ν Ωi dt +√
2D dBt)(3)
Remark. eq. (3) + “ν∆t = 1” ⇒ eq. (2)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 17/ 37
Introduction PTW model Vicsek model Numerical schemes
Vicsek model (’95)
Discrete dynamics:
RΩi
xiωi
xn+1i = xn
i +∆t ωni
ωn+1i = Ω
ni + ε
(2)
with Ωni =
∑|xj−xi |<R ωn
j∣∣∣∑|xj−xi |<R ωnj
∣∣∣ , ε noise.
Continuous dynamics:
dxidt = ωi
dωi = (Id− ωi ⊗ ωi )( ν Ωi dt +√
2D dBt)(3)
Remark. eq. (3) + “ν∆t = 1” ⇒ eq. (2)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 17/ 37
Introduction PTW model Vicsek model Numerical schemes
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 18/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Under the hypothesis of propagation of chaos3, the density ofparticles f (t, x , ω) satisfies:
∂t f + ω · ∇x f +∇ω · (F f ) = D∆ωf ,
with :
F (x , ω) = (Id− ω ⊗ ω) νΩ(x) , Ω(x) =J(x)
|J(x)|
J(x) =
∫|y−x |<R, ω∗∈S1
ω∗ f (y , ω∗) dy dω∗
The alignment is expressed by the operator ∇ω · Ff ,
The randomness is expressed by D∆ωf .
3Sznitman, Saint-Flour (89)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 19/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Under the hypothesis of propagation of chaos3, the density ofparticles f (t, x , ω) satisfies:
∂t f + ω · ∇x f +∇ω · (F f ) = D∆ωf ,
with :
F (x , ω) = (Id− ω ⊗ ω) νΩ(x) , Ω(x) =J(x)
|J(x)|
J(x) =
∫|y−x |<R, ω∗∈S1
ω∗ f (y , ω∗) dy dω∗
The alignment is expressed by the operator ∇ω · Ff ,
The randomness is expressed by D∆ωf .
3Sznitman, Saint-Flour (89)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 19/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Under the hypothesis of propagation of chaos3, the density ofparticles f (t, x , ω) satisfies:
∂t f + ω · ∇x f +∇ω · (F f ) = D∆ωf ,
with :
F (x , ω) = (Id− ω ⊗ ω) νΩ(x) , Ω(x) =J(x)
|J(x)|
J(x) =
∫|y−x |<R, ω∗∈S1
ω∗ f (y , ω∗) dy dω∗
The alignment is expressed by the operator ∇ω · Ff ,
The randomness is expressed by D∆ωf .
3Sznitman, Saint-Flour (89)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 19/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Finally, f satisfies:
∂t f + ω · ∇x f = Q(f ) (4)
with: Q(f ) = −∇ω · (Ff ) + D∆ωf .
The equilibrium of Q(f ) (i.e. Qf = 0) are the Von Misesdistributions:
MΩ(ω) = C exp
(ω · Ω
T
)where T = D/ν and Ω is an arbitrary direction.
The total momentum is not preserved by the operator:∫ω
Q(f )ω dω 6= 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 20/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Finally, f satisfies:
∂t f + ω · ∇x f = Q(f ) (4)
with: Q(f ) = −∇ω · (Ff ) + D∆ωf .
The equilibrium of Q(f ) (i.e. Qf = 0) are the Von Misesdistributions:
MΩ(ω) = C exp
(ω · Ω
T
)where T = D/ν and Ω is an arbitrary direction.
The total momentum is not preserved by the operator:∫ω
Q(f )ω dω 6= 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 20/ 37
Introduction PTW model Vicsek model Numerical schemes
Kinetic equation
Finally, f satisfies:
∂t f + ω · ∇x f = Q(f ) (4)
with: Q(f ) = −∇ω · (Ff ) + D∆ωf .
The equilibrium of Q(f ) (i.e. Qf = 0) are the Von Misesdistributions:
MΩ(ω) = C exp
(ω · Ω
T
)where T = D/ν and Ω is an arbitrary direction.
The total momentum is not preserved by the operator:∫ω
Q(f )ω dω 6= 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 20/ 37
Introduction PTW model Vicsek model Numerical schemes
0
0.2
0.4
0.6
0.8
1
−3 −2 −1 0 1 2 3
Pro
bab
ilit
y d
ensi
ty
Theoretic
Particles
θ
Figure: Local distribution of velocity f (Left) for a simulation in a smalldomain (Right).
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 21/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Step 1. Hydrodynamic scaling: t ′ = εt, x ′ = εx .In these macroscopic variables, f ε satisfies:
∂t fε + ω · ∇x f ε =
1
εQ(f ε). (5)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
⇒ f 0 is an equilibrium: f 0(x , ω) = ρ0(x)MΩ0(x)(ω).
Step 3. Integrate (5) against the collisional invariants∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]ψ dω
with ψ such that∫ω Q(f )ψ dω = 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 22/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Step 1. Hydrodynamic scaling: t ′ = εt, x ′ = εx .In these macroscopic variables, f ε satisfies:
∂t fε + ω · ∇x f ε =
1
εQ(f ε). (5)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
⇒ f 0 is an equilibrium: f 0(x , ω) = ρ0(x)MΩ0(x)(ω).
Step 3. Integrate (5) against the collisional invariants∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]ψ dω
with ψ such that∫ω Q(f )ψ dω = 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 22/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Step 1. Hydrodynamic scaling: t ′ = εt, x ′ = εx .In these macroscopic variables, f ε satisfies:
∂t fε + ω · ∇x f ε =
1
εQ(f ε). (5)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
⇒ f 0 is an equilibrium: f 0(x , ω) = ρ0(x)MΩ0(x)(ω).
Step 3. Integrate (5) against the collisional invariants∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]ψ dω
with ψ such that∫ω Q(f )ψ dω = 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 22/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Step 1. Hydrodynamic scaling: t ′ = εt, x ′ = εx .In these macroscopic variables, f ε satisfies:
∂t fε + ω · ∇x f ε =
1
εQ(f ε). (5)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
⇒ f 0 is an equilibrium: f 0(x , ω) = ρ0(x)MΩ0(x)(ω).
Step 3. Integrate (5) against the collisional invariants∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]ψ dω
with ψ such that∫ω Q(f )ψ dω = 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 22/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Step 1. Hydrodynamic scaling: t ′ = εt, x ′ = εx .In these macroscopic variables, f ε satisfies:
∂t fε + ω · ∇x f ε =
1
εQ(f ε). (5)
Step 2. Hilbert expansion: f ε = f 0 + εf 1 + ...
⇒ f 0 is an equilibrium: f 0(x , ω) = ρ0(x)MΩ0(x)(ω).
Step 3. Integrate (5) against the collisional invariants∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]ψ dω
with ψ such that∫ω Q(f )ψ dω = 0.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 22/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a if for every f satisfying∫ω ωf dω // Ω∫
ωQ(f )ψ dω = 0 ⇒
∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ =
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ =
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ =
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ = 1
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ = 1
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ =
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Derivation of a hyperbolic system
Problem: Only one quantity is preserved by Q.
Momentum is not preserved by the dynamics
Def. ψ is a collisional invariant if for every f satisfying∫ω ωf dω // Ω∫ω
Q(f )ψ dω = 0 ⇒∫ω
f Q∗Ωf(ψ) dω = 0 ⇒ ψ =
1ϕΩ(ω)
with ϕΩ a solution of: Q∗(ϕΩ) = ω × Ω.
Then, we can integrate the kinetic equation:∫ω
[∂t fε + ω · ∇x f ε =
1
εQ(f ε) ]
(1
ϕΩε(ω)
)dω
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 23/ 37
Introduction PTW model Vicsek model Numerical schemes
Hyperbolic system
Thm.4 The distribution f ε solution of (5) satisfies:
f εε→0 ρMΩ(ω)
with:Hyperbolic system
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
where c1, c2 and λ depend on T = D/ν.
Remarks:
the system obtained is hyperbolic...
...but non-conservative (due to the constraint |Ω| = 1)
ρ and Ω have different convection speeds (c1 6= c2).4Degond, M., Math. Models Methods Appli. Sci. (2008)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 24/ 37
Introduction PTW model Vicsek model Numerical schemes
Hyperbolic system
Thm.4 The distribution f ε solution of (5) satisfies:
f εε→0 ρMΩ(ω)
with:Hyperbolic system
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
where c1, c2 and λ depend on T = D/ν.
Remarks:
the system obtained is hyperbolic...
...but non-conservative (due to the constraint |Ω| = 1)
ρ and Ω have different convection speeds (c1 6= c2).4Degond, M., Math. Models Methods Appli. Sci. (2008)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 24/ 37
Introduction PTW model Vicsek model Numerical schemes
Hyperbolic system
Thm.4 The distribution f ε solution of (5) satisfies:
f εε→0 ρMΩ(ω)
with:Hyperbolic system
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
where c1, c2 and λ depend on T = D/ν.
Remarks:
the system obtained is hyperbolic...
...but non-conservative (due to the constraint |Ω| = 1)
ρ and Ω have different convection speeds (c1 6= c2).4Degond, M., Math. Models Methods Appli. Sci. (2008)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 24/ 37
Introduction PTW model Vicsek model Numerical schemes
Hyperbolic system
Thm.4 The distribution f ε solution of (5) satisfies:
f εε→0 ρMΩ(ω)
with:Hyperbolic system
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
where c1, c2 and λ depend on T = D/ν.
Remarks:
the system obtained is hyperbolic...
...but non-conservative (due to the constraint |Ω| = 1)
ρ and Ω have different convection speeds (c1 6= c2).4Degond, M., Math. Models Methods Appli. Sci. (2008)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 24/ 37
Introduction PTW model Vicsek model Numerical schemes
Hyperbolic system
Thm.4 The distribution f ε solution of (5) satisfies:
f εε→0 ρMΩ(ω)
with:Hyperbolic system
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
where c1, c2 and λ depend on T = D/ν.
Remarks:
the system obtained is hyperbolic...
...but non-conservative (due to the constraint |Ω| = 1)
ρ and Ω have different convection speeds (c1 6= c2).4Degond, M., Math. Models Methods Appli. Sci. (2008)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 24/ 37
Introduction PTW model Vicsek model Numerical schemes
Applications
Combine PTW and Vicsek model
Degond, M., A Macroscopic Model for a System of Swarming AgentsUsing Curvature Control, J. Stat. Phys. (2011).
Extend the method for attraction-alignment-repulsion model
Liu, Degond, M., Panferov, Hydrodynamic models of self-organizeddynamics, submitted (2011).
Perspectives
Study numerically the kinetic equation
joint work with I. Gamba, J. Haack
Corroborate the macroscopic model with experimental data
project with I. Couzin, S. Garnier
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 25/ 37
Introduction PTW model Vicsek model Numerical schemes
Applications
Combine PTW and Vicsek model
Degond, M., A Macroscopic Model for a System of Swarming AgentsUsing Curvature Control, J. Stat. Phys. (2011).
Extend the method for attraction-alignment-repulsion model
Liu, Degond, M., Panferov, Hydrodynamic models of self-organizeddynamics, submitted (2011).
Perspectives
Study numerically the kinetic equation
joint work with I. Gamba, J. Haack
Corroborate the macroscopic model with experimental data
project with I. Couzin, S. Garnier
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 25/ 37
Introduction PTW model Vicsek model Numerical schemes
Outline
1 PTW modelExperiments and modelDerivation of a diffusion equation
2 Vicsek modelThe modelDerivation of a hyperbolic system
3 Numerical schemesSplitting methodParticle simulationsMicro vs macro
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 26/ 37
Introduction PTW model Vicsek model Numerical schemes
Numerical simulation
We want to numerically solve the macroscopic Vicsek (MV) model:
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
Two difficulties:
The model is non-conservative...
...and has a geometric constraint
⇒ No available theory to deal with this system.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 27/ 37
Introduction PTW model Vicsek model Numerical schemes
Numerical simulation
We want to numerically solve the macroscopic Vicsek (MV) model:
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
Two difficulties:
The model is non-conservative...
...and has a geometric constraint
⇒ No available theory to deal with this system.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 27/ 37
Introduction PTW model Vicsek model Numerical schemes
Numerical simulation
We want to numerically solve the macroscopic Vicsek (MV) model:
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
Two difficulties:
The model is non-conservative...
...and has a geometric constraint
⇒ No available theory to deal with this system.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 27/ 37
Introduction PTW model Vicsek model Numerical schemes
Numerical simulation
We want to numerically solve the macroscopic Vicsek (MV) model:
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
Two difficulties:
The model is non-conservative...
...and has a geometric constraint
⇒ No available theory to deal with this system.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 27/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,ρ(∂tΩ + c2(Ω · ∇x)Ω) + λ (Id− Ω⊗ Ω)∇xρ = 0,|Ω| = 1
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,
∂t(ρΩ) + c2∇x · (ρΩ⊗ Ω) + λ∇xρ =ρ
η(1− |Ω|2)Ω,
|Ω| = 1
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,
∂t(ρΩ) + c2∇x · (ρΩ⊗ Ω) + λ∇xρ =ρ
η(1− |Ω|2)Ω,
|Ω| = 1
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,
∂t(ρΩ) + c2∇x · (ρΩ⊗ Ω) + λ∇xρ =ρ
η(1− |Ω|2)Ω,
|Ω| = 1
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Splitting method
The main idea of this method is to replace the geometricconstraint (|Ω| = 1) by a relaxation operator:
∂tρ+ c1∇x · (ρΩ) = 0,
∂t(ρΩ) + c2∇x · (ρΩ⊗ Ω) + λ∇xρ =ρ
η(1− |Ω|2)Ω,
|Ω| = 1
In the limit η → 0, we recover the original MV model.
To solve numerically this system, we proceed in two steps(splitting):
First, we solve the conservative part (left-hand-side)...
...and then the relaxation part.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 28/ 37
Introduction PTW model Vicsek model Numerical schemes
Other numerical methods
In one direction, the system is written:
∂tρ+ c1∂x (ρ cos θ) = 0
∂tθ + c2 cos θ ∂xθ − λsin θ
ρ∂xρ = 0. (6)
Multiplying (6) by 1/ sin θ and integrating in θ, we find aconservative formulation of the MV model.
Solving the conservative formulation gives another method⇒ Conservative method
Remark. Other methods can be developed using the“non-conservative” form of the MV model (e.g. upwind scheme).
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 29/ 37
Introduction PTW model Vicsek model Numerical schemes
Other numerical methods
In one direction, the system is written:
∂tρ+ c1∂x (ρ cos θ) = 0
∂tθ + c2 cos θ ∂xθ − λsin θ
ρ∂xρ = 0. (6)
Multiplying (6) by 1/ sin θ and integrating in θ, we find aconservative formulation of the MV model.
Solving the conservative formulation gives another method⇒ Conservative method
Remark. Other methods can be developed using the“non-conservative” form of the MV model (e.g. upwind scheme).
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 29/ 37
Introduction PTW model Vicsek model Numerical schemes
Other numerical methods
In one direction, the system is written:
∂tρ+ c1∂x (ρ cos θ) = 0
∂tθ + c2 cos θ ∂xθ − λsin θ
ρ∂xρ = 0. (6)
Multiplying (6) by 1/ sin θ and integrating in θ, we find aconservative formulation of the MV model.
Solving the conservative formulation gives another method⇒ Conservative method
Remark. Other methods can be developed using the“non-conservative” form of the MV model (e.g. upwind scheme).
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 29/ 37
Introduction PTW model Vicsek model Numerical schemes
Other numerical methods
In one direction, the system is written:
∂tρ+ c1∂x (ρ cos θ) = 0
∂tθ + c2 cos θ ∂xθ − λsin θ
ρ∂xρ = 0. (6)
Multiplying (6) by 1/ sin θ and integrating in θ, we find aconservative formulation of the MV model.
Solving the conservative formulation gives another method⇒ Conservative method
Remark. Other methods can be developed using the“non-conservative” form of the MV model (e.g. upwind scheme).
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 29/ 37
Introduction PTW model Vicsek model Numerical schemes
Simulations 1
The numerical schemes agree with each other on rarefaction waves(smooth solutions)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 30/ 37
Introduction PTW model Vicsek model Numerical schemes
Simulations 2
However, the numerical schemes disagree when the solution is ashock wave (non-smooth solutions)
Question : What is the correct solution ? Do we have it ?
⇒ Go back to the microscopic model...
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 31/ 37
Introduction PTW model Vicsek model Numerical schemes
Simulations 2
However, the numerical schemes disagree when the solution is ashock wave (non-smooth solutions)
Question : What is the correct solution ? Do we have it ?
⇒ Go back to the microscopic model...
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 31/ 37
Introduction PTW model Vicsek model Numerical schemes
Simulations 2
However, the numerical schemes disagree when the solution is ashock wave (non-smooth solutions)
Question : What is the correct solution ? Do we have it ?
⇒ Go back to the microscopic model...
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 31/ 37
Introduction PTW model Vicsek model Numerical schemes
Particle simulations
Since there is no theoretical solution to test our numericalsimulations, we use the microscopic Vicsek model as a benchmark:
dxεkdt
= ωεk ,
dωεk =1
ε(Id− ωεk ⊗ ωεk)(νΩ
εk dt +
√2D dBt),
with
Ωεk =
Jεk|Jεk |
, Jεk =∑
j , |xεj −xεk |≤εR
ωεj .
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 32/ 37
Introduction PTW model Vicsek model Numerical schemes
Particle simulations
Since there is no theoretical solution to test our numericalsimulations, we use the microscopic Vicsek model as a benchmark:
dxεkdt
= ωεk ,
dωεk =1
ε(Id− ωεk ⊗ ωεk)(νΩ
εk dt +
√2D dBt),
with
Ωεk =
Jεk|Jεk |
, Jεk =∑
j , |xεj −xεk |≤εR
ωεj .
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 32/ 37
Introduction PTW model Vicsek model Numerical schemes
We use Riemann problem as initial condition.
Figure: Density ρ: Micro (left) and Macro (right)
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 33/ 37
Introduction PTW model Vicsek model Numerical schemes
We take a cross section of the distribution in the x-direction:
Figure: macro. equation (line) and micro. equation (dot) at time t = 2.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 34/ 37
Introduction PTW model Vicsek model Numerical schemes
We take a cross section of the distribution in the x-direction:
Figure: macro. equation (line) and micro. equation (dot) at time t = 4.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 34/ 37
Introduction PTW model Vicsek model Numerical schemes
Micro vs macro
We compare the solutions of the MV model with the particles forthe shock-wave solution:
The splitting method has the “correct speed”.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 35/ 37
Introduction PTW model Vicsek model Numerical schemes
Micro vs macro
We compare the solutions of the MV model with the particles forthe shock-wave solution:
The splitting method has the “correct speed”.
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 35/ 37
Introduction PTW model Vicsek model Numerical schemes
Contact discontinuity
For an initial condition given as a contact discontinuity, a weaksolution is given by a traveling wave.We observe numerically another type of solution5:
5M., Navoret, SIAM Multiscale Modeling & Simulation (2011)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 36/ 37
Introduction PTW model Vicsek model Numerical schemes
Contact discontinuity
For an initial condition given as a contact discontinuity, a weaksolution is given by a traveling wave.We observe numerically another type of solution5:
5M., Navoret, SIAM Multiscale Modeling & Simulation (2011)Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 36/ 37
Introduction PTW model Vicsek model Numerical schemes
General case
How about non-standard initial condition?
Sebastien Motsch (CSCAMM) Math. model. of self-organized dynamics 6 March 2012 37/ 37