72 citations · 72 across the 4 of their papers we have counts for
5 papers
Physics Informed Random Feature Neural Networks for Solving PDEs
Chi-An Chen, Chunyang Liao, Ming Zhong
Machine learning-based partial differential equations (PDEs) solvers have attracted significant attention in recent years. Most progress in this area has been driven by deep neural…
SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations
Xingzhuo Chen, Ulisses Braga-Neto, Lifan Wang +5
We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physic…
Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
We present a series of empirical results of the application of semi-supervised label propagation techniques in training physics-informed machine learning methods. This includes sel…
Stability in Training PINNs for Stiff PDEs: Why Initial Conditions Matter
Baoli Hao, Chun Liu, Ulisses Braga-Neto +2
Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through…
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity
E. J. R. Coutinho, M. Dall'Aqua, L. McClenny +3
Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it h…