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Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
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 self-training of physics-informed neural networks and physics-informed Gaussian processes in isolation, and the integration
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