Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
Machine Learning
2024-04-10 v1
Abstract
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. This includes self-training of physics-informed neural networks and physics-informed Gaussian processes in isolation, and the integration of the two via co-training. We demonstrate via extensive numerical experiments how these methods can ameliorate the issue of propagating information forward in time, which is a common failure mode of physics-informed machine learning.
Keywords
Cite
@article{arxiv.2404.05817,
title = {Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes},
author = {Ming Zhong and Dehao Liu and Raymundo Arroyave and Ulisses Braga-Neto},
journal= {arXiv preprint arXiv:2404.05817},
year = {2024}
}