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

• URL: http://arxiv.org/abs/2404.05817v1
• Date: Mon, 8 Apr 2024 18:41:55 GMT
• Title: Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
• Authors: Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto,
• Abstract summary: This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. We show how these methods can ameliorate the issue of propagating information forward in time.
• Score: 12.027710824379428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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