Structural Constraints for Physics-augmented Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912062869864448 |
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| author | Kuang, Simon Lin, Xinfan |
| author_facet | Kuang, Simon Lin, Xinfan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_05507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Structural Constraints for Physics-augmented Learning Kuang, Simon Lin, Xinfan Machine Learning Systems and Control 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. |
| title | Structural Constraints for Physics-augmented Learning |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2410.05507 |