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20212026
most citedA hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification

24 citations · 43 across the 8 of their papers we have counts for

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cs.LG2026

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…

cs.LG2026

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…