Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural networks (PINNs) learn PDE solutions from governing equations. However, accurately capturing temporal evolution remains challenging. Recent sequence-model-based approaches parameterize time evolution using general-purpose sequence models, which capture temporal dependencies but do not explicitly encode the structured dynamics of PDE solutions. In addition, their memory requirements can scale unfavorably with sequence length and resolution, limiting applicability in large-scale or high-dimensional settings. This work introduces a PINN approa...
| Authors | Abhishek Chandra, Taniya Kapoor |
|---|---|
| Primary category | cs.NE |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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