78 citations · 381 across the 20 of their papers we have counts for
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A Unified Weighted-Loss Physics-Informed Neural Network for Boundary Layer Problems in Singularly Perturbed PDEs
Wei-Fan Hu, Shi-Xiang Zhong, Po-Wen Hsieh +2
Singularly perturbed partial differential equations arise in many applications, including magnetohydrodynamic duct flows, chemical reaction transport systems, and Poisson Boltzmann…
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2
We propose a physics-informed consistency modeling framework for solving partial differential equations (PDEs) via fast, few-step generative inference. We identify a key stability…
TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
Chen-Yang Dai, Che-Chia Chang, Te-Sheng Lin +2
Physics-informed neural networks (PINNs) solve time-dependent partial differential equations (PDEs) by learning a mesh-free, differentiable solution that can be evaluated anywhere…