Structural Constraints for Physics-augmented Learning
<|reference_start|>Structural Constraints for Physics-augmented Learning: When the physics is wrong, physics-informed machine learning becomes physics-misinformed machine learning. A powerful black-box model should not be able to conceal misconceived physics. We propose two criteria that can be used to assert integrity that a hybrid (physics plus black-box) model: 0) the black-box model should be unable to replicate the physical model, and 1) any best-fit hybrid model has the same physical parameter as a best-fit standalone physics model. We demonstrate them for a sample nonlinear mechanical system approximated by its small-signal linearization.<|reference_end|>
@article{kuang2024structural,
    title={Structural Constraints for Physics-augmented Learning},
    author={Simon Kuang and Xinfan Lin},
    journal={arXiv preprint arXiv:2410.05507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05507},
    primaryClass={cs.LG cs.SY eess.SY}
}