3 papers
astro-ph.HE2025
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…
cs.LG2024
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. This includes self-training of physics-informed neural networks and physic…
math.NA2024
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…