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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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physics.comp-ph2025

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-…

cs.LG2025

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…