Structure-preserving learning for multi-symplectic PDEs

TL;DR

This paper introduces a non-intrusive, energy-preserving machine learning approach for inferring reduced-order models of multi-symplectic PDEs that maintains key conservation laws without requiring fully discrete operators.

Contribution

It proposes a novel data-driven, energy-preserving method for multi-symplectic PDEs that does not depend on discrete operators and can generalize beyond training data.

Findings

Successfully preserves local energy conservation laws.

Effective on equations like wave, KdV, and Zakharov-Kuznetsov.

Demonstrates good generalization outside training interval.

Abstract

This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations (PDEs). The vast majority of energy-preserving reduced-order methods use symplectic Galerkin projection to construct reduced-order Hamiltonian models by projecting the full models onto a symplectic subspace. However, symplectic projection requires the existence of fully discrete operators, and in many cases, such as black-box PDE solvers, these operators are inaccessible. In this work, we propose an energy-preserving machine learning method that can infer the dynamics of the given PDE using data only, so that the proposed framework does not depend on the fully discrete operators. In this context, the proposed method is non-intrusive. The proposed method is grey box in the sense that it requires only some basic knowledge of the multi-symplectic model at the partial differential equation level. We prove that the proposed method satisfies spatially discrete local energy conservation and preserves the multi-symplectic conservation laws. We test our method on the linear wave equation, the Korteweg-de Vries equation, and the Zakharov-Kuznetsov equation. We test the generalization of our learned models by testing them far outside the training time interval.