Generative Modeling of Stochastic Dynamics for Long-Time Evolution

Yang-yang Tan; Jinyang Li; Lingxiao Wang

cond-mat.stat-mechSep 30, 2026

Generative Modeling of Stochastic Dynamics for Long-Time Evolution

Authors: Yang-yang Tan, Jinyang Li, Lingxiao Wang

Organizations: Institute for Physics of Intelligence, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan · RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Wako, Saitama 351-0198, Japan · KEK Theory Center, Institute of Particle and Nuclear Studies · Graduate University for Advanced Studies (SOKENDAI), Oho 1-1, Tsukuba, Ibaraki 305-0801, Japan

Abstract

Exact stochastic equations for non-equilibrium dynamics are rarely accessible. We show that the long-time evolution of stochastic dynamics can be predicted from configuration pairs at a fixed short time lag, without knowledge of the equation of motion. Generative diffusion models learn the finite-time transition kernel from these pairs, and iterating it propagates the dynamics far beyond the training lag. For two-dimensional Model B, the diffusive dynamics of a conserved order parameter, the learned kernels reproduce dynamic critical scaling and self-similar $t^{1/3}$ coarsening. Agreement with direct simulations persists on lattices twice the largest training size and for initial ensembles absent from training. For driven colloids in a periodic optical potential, ten minutes of measured trajectories suffice to predict the particle current and mean passage time over the next twenty minutes within experimental uncertainty. Short-time observations thus contain the information needed to predict emergent non-equilibrium dynamics at much longer times.

Figures & tables

Figure 1: Conditional transition kernel. The upper row shows the physical trajectory from $\phi_{0}$ to $\phi_{t}$ and onward in steps of $\delta t$ . In the lower row, the present field $\phi_{t}$ conditions a score network that guides reverse diffusion from Gaussian noise at $s=1$ to an increment $\Delta\phi_{s=0}\sim p_{\theta}(\Delta\phi\mid\phi_{t})$ . Adding this increment to $\phi_{t}$ yields $\phi_{t+\delta t}$ . Repeated sampling generates $\phi_{t+n\delta t}$ . The snapshots illustrate coarsening deep in the broken phase.

Figure 2: Long-time prediction of the learned kernel from the diffusion model (DM) against a high-precision stochastic equation solver (SOSRA [ 22 ] ) for Model B at the critical point ( $\kappa=0.34034$ , $\lambda=1$ , $L=32$ , and $\delta t=10$ ). (a) Field configurations at $t=0$ , $10$ , $50$ , $100$ , $500$ , and $2000$ for equilibrium HMC (upper pair) and non-equilibrium Gaussian (lower pair, with the spatial mean of the paired HMC field) initial ensembles. Within each pair, SOSRA and DM are shown above and below, respectively, and start from the same field at $t=0$ . (b) Equal-time power $G_{1}(t)$ of the smallest diagonal mode. Dotted lines with bands show SOSRA, and points show DM. (c) Mean-field drift $\langle|M(t)-M(0)|\rangle$ . Both dynamics conserve $M$ to single-precision accuracy. Line and marker styles are as in (b).

Figure 3: Critical relaxation at

\kappa=0.34034

\lambda=1

, and

L=32

. The diagonal-mode correlations collapse when plotted against

x=\Delta t/\tau_{n}

. Colored markers show the DM results, with error bars denoting standard errors. The dotted black curve and the dashed blue curve show fits of Eq. 11 to the SOSRA and DM data, respectively. The gray dash-dotted curve is

e^{-x}

. The inset shows the relaxation times versus lattice momentum, yielding effective exponents

z=3.792(19)

for the DM and

z=3.812(20)

for SOSRA.

Figure 4: Coarsening deep in the broken phase ( $\kappa=0.85$ , $\lambda=1$ ). The kernel was trained only on synthetic field configurations at $L\leq 64$ and applied at $L=128$ from a disordered initial state. (a) DM configurations at $t=10$ , $100$ , and $1000$ . (b) Collapse of the sign-field correlation $C(r,t)$ for $t=100$ – $1000$ . The insets compare the domain size $L(t)$ , defined by the first zero of $C(r,t)$ , with the direct SOSRA result and show their ratio. The black dotted line shows SOSRA, and blue open circles show DM. The gray dashed line denotes the $t^{1/3}$ growth law. The SOSRA and DM ensembles contain $128$ and $32$ trajectories, respectively.

	Wall time (s)
Method	Eq.	Noneq.
DPM-Solver-v3 (This work)	$3.24$	$3.26$
SOSRA (This work)	$1.30$	$3.28$
SOSRA (StochasticDiffEq.jl) [ 22 , 51 ]	$12.36$	$30.67$
ShARK (Diffrax) [ 52 , 53 ]	$5.56$	$17.76$
EM (Diffrax) [ 54 , 53 ]	$19.45$	$38.75$

Table 1: Wall time for evolving 512 configurations on an

L=64

lattice from

t=0

100

, for the equilibrium (Eq.) HMC ensemble and the non-equilibrium (Noneq.) quench ensemble of Fig. 4 . Details are given in Sec. S6.1 and Sec. S6.2 .

\fnum@figure : Conserved transport during phase ordering on an $L=128$ lattice at $\kappa=0.85$ and $\lambda=1$ . All panels show the same enlarged region of one SOSRA trajectory. (a,b) Field $\phi$ at $t=100$ and $300$ . The dotted line in (b) marks $\phi(t=100)=0$ . (c) Colors show the field change $\Delta\phi=\phi(300)-\phi(100)$ . Selected streamlines show the direction of the integrated current $\mathbf{J}=\int_{100}^{300}\mathbf{j}\,dt$ , including the noise contribution in Eq. S8 . Very weak currents are omitted from the streamline overlay. The fixed box $\Omega$ encloses a shrinking domain. Fields and current are smoothed with the same periodic Gaussian of standard deviation two lattice spacings, preserving the discrete continuity relation $\Delta\phi+\nabla\cdot\mathbf{J}=0$ .

\fnum@figure : Time-origin averaged mode correlations

\rho_{n}(\Delta t)

of the first four diagonal modes on the equilibrium ensemble of Fig. 2 . Dotted lines with bands show SOSRA, and open markers show the DM kernel trained on Gaussian fields. All time origins from the

t=2000

trajectories are included. Bands and error bars are trajectory jackknife standard errors over

1024

trajectories.

\fnum@figure : Effective decay rate

\Gamma_{\rm eff}=-d\ln\rho_{n}/dx

of the DM scaling function against

1/x=\tau_{n}/\Delta t

for the data of Fig. 3 . Open markers show the four resolved diagonal modes. The dashed curve is a separate tail fit

1/(2x)+\lambda_{0}

for

x\geq 2

. The dash-dotted curve is the logarithmic derivative of the fit in Eq. 11 . The dotted lines mark a pure exponential decay,

\Gamma_{\rm eff}=1

, the short-time rate

r

fixed by the static amplitudes through Eq. S17 , and the long-time rate

\lambda_{0}

from the tail fit.

\fnum@figure : Dynamic scaling function of the structure factor during coarsening. Structure factor of the sign field for the

L=128

DM trajectories of Fig. 4 , rescaled by the domain size

L(t)

as in Eq. S22 . The dashed line is the Porod law

\hat{k}^{-3}

with fitted amplitude. Error bars are standard errors over the

32

trajectories.

\fnum@figure : Reverse-sampling resolution at

L=64

. Relaxation times are extracted by joining the statistically resolved segments of time-origin-averaged correlations on output grids with spacings

\delta t=0.5

up to

t=50

and

\delta t=10

up to

t=1000

. We compare SOSRA, the learned kernels sampled with 2000 EM steps, and the same kernels sampled with DPM-Solver-v3 using 32 NFE per physical transition. Only the commonly resolved modes

n=2,\ldots,8

are shown. The dotted line for SOSRA and dashed lines for the learned kernels are power-law fits to

\tau_{n}\propto\hat{k}_{n}^{-z}

. Error bars and the parenthesized uncertainties of

z

are trajectory-bootstrap standard errors.

\fnum@figure : Time-origin-averaged diagonal-mode correlations

\rho_{n}(\Delta t)

on the equilibrium ensemble at

L=32

. Panels (a)–(d) show

n=1,\ldots,4

for kernels trained at

L=32

and at

L=8,16,32

, with training lag

\delta t=10

. Panels (e)–(h) show

n=5,\ldots,8

for both kernels with

\delta t=0.5

. Dotted lines without markers show SOSRA on the corresponding time grid, squares the kernel trained at

L=32

, and circles the kernel trained at

L=8,16,32

. For the kernels, dashed and solid lines denote

\delta t=10

and

0.5

. The upper row is shown up to

\Delta t=710

. In the lower row, the kernel trained at

L=32

is shown up to

\Delta t=5.5

, and the other curves up to

\Delta t=10

. Every point averages at least 30 time origins. Error bars are leave-one-trajectory-out jackknife standard errors over 512 trajectories for training at

L=8,16,32

and over 1024 trajectories for training at

L=32

\fnum@figure : One-step diagonal-mode transfer tests at

L=32

and

L=64

. Panels (a) and (b) show

\rho_{n}(\delta t)

L=32

and

64

, respectively. Each correlation is evaluated at the single-step separation

\Delta t=\delta t

. The

L=32

initial fields are drawn from the training set, while

L=64

was excluded from training. Each panel compares

\delta t=0.5

and

10

for SOSRA and the two diffusion kernels. Dotted lines without markers show SOSRA at both time separations. For the kernels, solid lines denote

\delta t=0.5

and dashed lines

\delta t=10

. Squares show the kernel trained only at

L=32

, and circles show the kernel trained on mixed

L=8,16,32

lattices. Error bars on the kernel results are leave-one-trajectory-out jackknife standard errors over 512 trajectories.

Method	Step or NFE	Time (s)	$S/V$	$\phi_{2}$	$\phi_{4}$	$\epsilon$
DPM-Solver-v3 (This work)	32 NFE per 10	$3.26$	$-0.078$	$+0.098$	$+0.183$	$0.128$
SOSRA (This work)	$0.0025$	$3.28$	$-0.075$	$-0.061$	$-0.157$	$0.107$
SOSRA (StochasticDiffEq.jl) [ 22 , 51 ]	$0.0025$	$30.67$	$-0.146$	$-0.021$	$-0.102$	$0.104$
ShARK (Diffrax) [ 52 , 53 ]	$0.00125$	$17.76$	$+0.007$	$-0.052$	$-0.130$	$0.081$
EM (Diffrax) [ 54 , 53 ]	$0.0002$	$38.75$	$+0.209$	$-0.047$	$-0.025$	$0.125$
EM (StochasticDiffEq.jl) [ 54 , 51 ]	$0.0002$	$256.44$	$+0.163$	$-0.005$	$+0.049$	$0.098$

Table S1: Accuracy at

t=100

and wall time for the non-equilibrium benchmark. Observable entries are signed relative differences from the

h=0.0001

SOSRA reference in percent. The last column is Eq. ( S24 ). All rows use one NVIDIA GH200.

	Undriven			Driven
	Data	DM	Langevin	Data	DM	Langevin
$P(\Delta n=-1)$ per step	$0.0157$	$0.0154$	$0.0161$	$0.0728$	$0.0735$	$0.0763$
$P(\Delta n=+1)$ per step	$0.0168$	$0.0170$	$0.0161$	$0.0047$	$0.0047$	$0.0043$
$\bar{v}$ [ $\mu$ m/s]	$0.0005(1)$	$0.0006$	$0.0000$	$-0.0282(3)$	$-0.0284$	$-0.0298$
$D_{{\rm eff},x}$ [ $\mu$ m 2 /s]	$0.0152$	$0.0163$	$0.0163$	$0.0430$	$0.0396$	$0.0408$
Passage time [s]	$610(15)$	$576$	$576$	$168(4)$	$166.5$	$157.3$

Table S2: Measured and predicted colloid transport at

10

s resolution. The comparison covers

50

min without driving and

20

min under driving, starting at recording time

10

min. Parentheses give standard errors over measured tracks. Model means use

32

replicas per initial position, with sampling errors omitted. Passage times include only trajectories reaching either

x_{\rm i}\pm\lambda

without driving, or

x_{\rm i}-\lambda

under driving, within the observation window.

\fnum@figure : Colloid transport without driving (upper row) and under driving (lower row). Symbols and lines as in Fig. End Matter . (a,b,d,e) Mean-square displacement along

x

and

y

over the time

\Delta t

elapsed since the common origin at

t=10

min. The gray line shows free diffusion

2D\,\Delta t

. (c,f) Mean transverse position against recording time. Shading shows the standard error of the mean displacement from

t=0

. The dotted line marks the end of training. The later transverse drift in the driven data is absent from both models.

\fnum@figure : Distributions of the net displacement

\Delta x

from

t=10

min after

1

5

20

, and

50

min of undriven motion (upper row) and after

1

5

10

, and

20

min of driven motion (lower row). Panels (d) and (h) repeat Fig. End Matter (b,f). Bins have width

\lambda/4

and are centered at integer multiples of

\lambda/4

. Symbols and lines as in Fig. End Matter .

\fnum@figure : Periodic density and transition-path times of the colloid trajectories, undriven (upper row) and driven (lower row). (a,e)

-\ln p(x)

, the negative logarithm of the position probability density folded onto one spatial period. The coordinate

x

measures distance from the barrier top along the drive direction. Gray open circles show experimental data from the training window (

t=0

–

10

min). Black open circles show data from the subsequent prediction window (

t=10

–

60

min in (a) and

t=10

–

30

min in (e)). Blue solid and orange dashed lines show the DM and Langevin predictions over the corresponding prediction window. At equilibrium,

-\ln p(x)

equals

U(x)/(k_{B}T)

up to an additive constant. This identification does not hold under driving. Dotted lines mark the milestones

A

and

B

0.5

and

3.0\,\mu

m. (b–d,f–h) Transition-path times between

A

and

B

10

s resolution. Black open circles and gray open squares show the data for

A\to B

and

B\to A

. Full and faint lines show DM and the Langevin reference for the two directions. (b,f) All paths. (c,g) Paths passing the well bottom between the milestones. (d,h) Paths passing the barrier top. Without driving,

56\%

of the

A\to B

paths and

60\%

of the

B\to A

paths pass the well bottom. Under driving these fractions are

99\%

and

2\%

Explore similar work

Capturing non-Markovian dynamics in non-equilibrium stochastic systems using flow matching
Jun 4, 2026Bhargav Sriram Siddani, John B. Bell, Alejandro L. Garcia +1Stochastic Dynamics Stochastic Differential Equations
Lawrence Berkeley National Laboratory · San Jose State University
Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise
Jul 21, 2026Arthur Bizzi, Olga FinkStochastic Differential Equations Stochastic Dynamics
EPFL, Imperial College · EPFL
Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models
Jun 25, 2025Xinghao Dong, Huchen Yang, Jin-Long WuMultiscale Dynamics Latent Dynamics
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

Jun 4, 2026, cs.LGJ/K move · Enter open · S save

Capturing non-Markovian dynamics in non-equilibrium stochastic systems using flow matching

Bhargav Sriram Siddani, John B. Bell, Alejandro L. Garcia, Ishan Srivastava

Lawrence Berkeley National Laboratory · San Jose State University

Stochastic Dynamics Stochastic Differential Equations Statistical Physics Fluid Dynamics

Open paper AskPDF arXiv

Hydrodynamic models of stochastic particle systems represented by coarse-grained stochastic partial differential equations (SPDE), such as the regularized Dean-Kawasaki (DK) equation, do not accurately capture the short-time system dynamics that is dominated by non-Markovian effects, and low particle density regimes where the distributions are highly non-Gaussian. We develop a generative flow matching method that directly models the probability distribution of fluxes from particle simulations that explicitly incorporates non-Markovian and non-Gaussian effects. As a demonstration, we use this method to simulate the Kramers first passage time problem for a system of non-interacting Brownian particles. We show the model accurately captures the short-time behavior and provides better predictions of the statistical moments of the number density when compared against the solution of the Markovian baseline, regularized DK equation.

Abstract

Explore similar work

Capturing non-Markovian dynamics in non-equilibrium stochastic systems using flow matching

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise

Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models