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Sidecar: A Structure-Preserving Framework for Solving Partial Differential Equations with Neural Networks

Solving partial differential equations (PDEs) with neural networks (NNs) has shown great potential in various scientific and engineering fields. However, most existing NN solvers mainly focus on satisfying the given PDEs, without explicitly considering intrinsic physical properties such as mass conservation or energy dissipation. This limitation can result in unstable or nonphysical solutions, particularly in long-term simulations. To address this issue, we propose Sidecar, a novel framework that enhances the accuracy and physical consistency of existing NN solvers by incorporating structure-preserving knowledge. Inspired by the Time-Dependent Spectral Renormalization (TDSR) approach, our Sidecar framework introduces a small copilot network, which is trained to guide the existing NN solver in preserving physical structure. This framework is designed to be highly flexible, enabling the incorporation of structure-preserving principles from diverse PDEs into a wide range of NN solvers. Our experimental results on benchmark PDEs demonstrate the improvement of the existing neural network solvers in terms of accuracy and consistency with structure-preserving properties.

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