Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers 事件

Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers arXiv:2606.02623v1 Announce Type: cross Abstract: 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 g