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cs.LG2024
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…
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…