An Exterior-Embedding Neural Operator Framework for Preserving Conservation Laws

TL;DR

This paper introduces a universal framework that integrates with neural operators to enforce conservation laws strictly, improving accuracy and generalizability in solving PDEs governed by physical conservation principles.

Contribution

The Exterior-Embedded Conservation Framework (ECF) is a novel, adaptable approach that ensures neural operators adhere to conservation laws, addressing limitations of existing models and enhancing performance across diverse PDE problems.

Findings

Improves model accuracy by enforcing conservation laws.

Enhances generalizability across different PDE problems.

Theoretically proven to boost neural operator performance.

Abstract

Neural operators have demonstrated considerable effectiveness in accelerating the solution of time-dependent partial differential equations (PDEs) by directly learning governing physical laws from data. However, for PDEs governed by conservation laws(e.g., conservation of mass, energy, or matter), existing neural operators fail to satisfy conservation properties, which leads to degraded model performance and limited generalizability. Moreover, we observe that distinct PDE problems generally require different optimal neural network architectures. This finding underscores the inherent limitations of specialized models in generalizing across diverse problem domains. To address these limitations, we propose Exterior-Embedded Conservation Framework (ECF), a universal conserving framework that can be integrated with various data-driven neural operators to enforce conservation laws strictly in predictions. The framework consists of two key components: a conservation quantity encoder that extracts conserved quantities from input data, and a conservation quantity decoder that adjusts the neural operator's predictions using these quantities to ensure strict conservation compliance in the final output. Since our architecture enforces conservation laws, we theoretically prove that it enhances model performance. To validate the performance of our method, we conduct experiments on multiple conservation-law-constrained PDE scenarios, including adiabatic systems, shallow water equations, and the Allen-Cahn problem. These baselines demonstrate that our method effectively improves model accuracy while strictly enforcing conservation laws in the predictions.