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physics.comp-ph2024★ 5 cited
Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Weiwei Zhang, Wei Suo, Jiahao Song +1
In recent years, Physics-Informed Neural Networks (PINNs) have become a representative method for solving partial differential equations (PDEs) with neural networks. PINNs provide…
physics.comp-ph2024
PINN-MG: A Multigrid-Inspired Hybrid Framework Combining Iterative Method and Physics-Informed Neural Networks
Daiwei Dong, Wei Suo, Jiaqing Kou +1
Iterative methods are widely used for solving partial differential equations (PDEs). However, the difficulty in eliminating global low-frequency errors significantly limits their c…
physics.comp-ph2023★ 1 cited
A novel paradigm for solving PDEs: multi scale neural computing
Wei Suo, Weiwei Zhang
Numerical simulation is dominant in solving partial difference equations (PDEs), but balancing fine-grained grids with low computational costs is challenging. Recently, solving PDE…