Automatic network structure discovery of physics informed neural networks via knowledge distillation.

ABSTRACT: Partial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws. While physics-informed neural networks (PINNs) can simulate and invert PDEs, they mainly rely on external loss functions for physical constraints, making it difficult to automatically discover and embed physically consistent network structures. We propose a physics structure-informed neural network discovery method based on physics-informed distillation, which decouples physical and parameter regularization via staged optimization in teacher and student networks. After distillation, clustering and parameter reconstruction are used to extract and embed physically meaningful structures. Numerical experiments on Laplace,

SUBMITTER: Liu Z 

PROVIDER: S-EPMC12572318 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature