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arXiv · 2404.05817

Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes

Ming Zhong·Dehao Liu·Raymundo Arroyave·Ulisses Braga-Neto
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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.

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Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto. 2024-04-08. Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes. https://arxiv.org/abs/2404.05817

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