Structural Constraints for Physics-augmented Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 7 Oct 2024.
Builds on
11 of 25 references.
References · 11
- 2020Universal Differential Equations for Scientific Machine Learningcited by 350
- 2022On Neural Differential Equationscited by 265
- 2020Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Predictioncited by 137
- 2020Augmenting Physical Models with Deep Networks for Complex Dynamics Forecastingcited by 94
- 2018Neural Lander: Stable Drone Landing Control using Learned Dynamicscited by 82
- 2021A Framework for Machine Learning of Model Error in Dynamical Systemscited by 38
- 2019Learning Discrepancy Models From Experimental Datacited by 15
- 2022Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effectscited by 15
- 2023Learning About Structural Errors in Models of Complex Dynamical Systemscited by 13
- 2024Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Responsecited by 13
- 2023Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariatescited by 3
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Citing papers per year: 2026: 1.
1 paper.