Parametric model reduction of mean-field and stochastic systems via higher-order action matching

TL;DR

This paper introduces a parametric model reduction technique for stochastic and mean-field systems using higher-order action matching, enabling efficient and accurate population dynamics prediction across varying parameters.

Contribution

It develops a variational approach based on optimal transport to learn gradient fields representing population dynamics, improving over existing models.

Findings

Accurately predicts population dynamics over diverse parameters.

Outperforms diffusion-based and flow-based models.

Stabilizes training with Monte Carlo and quadrature techniques.

Abstract

The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned models can act as surrogates of classical numerical models to efficiently predict the system behavior over the physics parameters. Building on the Benamou-Brenier formula from optimal transport and action matching, we use a variational problem to infer parameter- and time-dependent gradient fields that represent approximations of the population dynamics. The inferred gradient fields can then be used to rapidly generate sample trajectories that mimic the dynamics of the physical system on a population level over varying physics parameters. We show that combining Monte Carlo sampling with higher-order quadrature rules is critical for accurately estimating the training objective from sample data and for stabilizing the training process. We demonstrate on Vlasov-Poisson instabilities as well as on high-dimensional particle and chaotic systems that our approach accurately predicts population dynamics over a wide range of parameters and outperforms state-of-the-art diffusion-based and flow-based modeling that simply condition on time and physics parameters.