Learning Moment Closure in Reaction-Diffusion Systems with Spatial Dynamic Boltzmann Distributions

TL;DR

This paper introduces a machine learning approach using Boltzmann distributions and differential equations to model complex reaction-diffusion systems in continuous space, enabling effective reduction and approximation of high-dimensional physical systems.

Contribution

The paper develops a physics-informed, neural network-based moment closure method employing differential equations and finite element basis functions for reaction-diffusion systems.

Findings

Accurately models reaction-diffusion systems with reduced dimensionality.

Demonstrates effective moment closure approximation on a chaotic oscillator.

Uses finite element basis functions for flexible physical system modeling.

Abstract

Many physical systems are described by probability distributions that evolve in both time and space. Modeling these systems is often challenging to due large state space and analytically intractable or computationally expensive dynamics. To address these problems, we study a machine learning approach to model reduction based on the Boltzmann machine. Given the form of the reduced model Boltzmann distribution, we introduce an autonomous differential equation system for the interactions appearing in the energy function. The reduced model can treat systems in continuous space (described by continuous random variables), for which we formulate a variational learning problem using the adjoint method for the right hand sides of the differential equations. This approach allows a physical model for the reduced system to be enforced by a suitable parameterization of the differential equations. In this work, the parameterization we employ uses the basis functions from finite element methods, which can be used to model any physical system. One application domain for such physics-informed learning algorithms is to modeling reaction-diffusion systems. We study a lattice version of the R{\"o}ssler chaotic oscillator, which illustrates the accuracy of the moment closure approximation made by the method, and its dimensionality reduction power.