24 citations · 43 across the 8 of their papers we have counts for
8 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-…
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…
A hybrid neural-network and MAC scheme for Stokes interface problems
Che-Chia Chang, Chen-Yang Dai, Wei-Fan Hu +2
In this paper, we present a hybrid neural-network and MAC (Marker-And-Cell) scheme for solving Stokes equations with singular forces on an embedded interface in regular domains. As…
A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification
Cheng Chang, Tieyong Zeng
Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data…
A Shallow Ritz Method for Elliptic Problems with Singular Sources
Ming-Chih Lai, Che-Chia Chang, Wei-Syuan Lin +2
In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features i…
Finite Difference Nets: A Deep Recurrent Framework for Solving Evolution PDEs
Cheng Chang, Liu Liu, Tieyong Zeng
There has been an arising trend of adopting deep learning methods to study partial differential equations (PDEs). In this paper, we introduce a deep recurrent framework for solving…