5 papers
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…
Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems
Che-Chia Chang, Te-Sheng Lin, Ming-Chih Lai
The Stefan problem is a classical free-boundary problem that models phase-change processes and poses computational challenges due to its moving interface and nonlinear temperature-…
A categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems
Wei-Fan Hu, Te-Sheng Lin, Yu-Hau Tseng +1
In this paper, we propose a categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems. The architecture comprises three hidde…
Consistency Training with Physical Constraints
Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2
We propose a physics-aware Consistency Training (CT) method that accelerates sampling in Diffusion Models with physical constraints. Our approach leverages a two-stage strategy: (1…