Summary of Structural Constraints For Physics-augmented Learning, by Simon Kuang and Xinfan Lin
Structural Constraints for Physics-augmented Learning
by Simon Kuang, Xinfan Lin
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes two criteria to ensure that hybrid models combining physics-based and black-box approaches do not perpetuate incorrect physical assumptions. A trustworthy hybrid model should be unable to replicate the underlying physical model, and any best-fit combination of the two models should have identical physical parameters. To demonstrate this concept, the authors apply their approach to a nonlinear mechanical system approximated by its small-signal linearization. The proposed criteria can help avoid “physics-misinformed” machine learning models that conceal misconceived physics. This work contributes to the development of more reliable and trustworthy hybrid modeling techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making sure machine learning models are based on correct physical laws. Sometimes, even if a model is good at predicting something, it might not be using the right rules of physics. The authors propose two ways to check if a model is using the right physical laws: 1) see if the model can’t replicate the underlying physical rules, and 2) make sure that any combination of the physical rules and machine learning model agrees on what’s happening in the system. They test this idea with a simple mechanical system and show how it helps ensure that machine learning models are trustworthy. |
Keywords
* Artificial intelligence * Machine learning
