Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
<|reference_start|>Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes: 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.<|reference_end|>
@article{zhong2024label,
    title={Label Propagation Training Schemes for Physics-Informed Neural Networks
  and Gaussian Processes},
    author={Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto},
    journal={arXiv preprint arXiv:2404.05817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05817},
    primaryClass={cs.LG}
}