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.
Cite
@article{arxiv.2410.05507,
title = {Structural Constraints for Physics-augmented Learning},
author = {Simon Kuang and Xinfan Lin},
journal= {arXiv preprint arXiv:2410.05507},
year = {2024}
}