Summary of Physics-enhanced Machine Learning: a Position Paper For Dynamical Systems Investigations, by Alice Cicirello
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
by Alice Cicirello
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 position paper explores Physics-Enhanced Machine Learning (PEML), a subfield that goes beyond traditional Machine Learning (ML) strategies to tackle challenges in dynamical systems. The need for PEML arises from limitations in data volume, accuracy of predictions, uncertainties, and explainability. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded, and physics-informed. The paper highlights the advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special kind of machine learning called Physics-Enhanced Machine Learning (PEML). It’s about using physics to make better predictions and decisions. The problem is that traditional machine learning can’t always get it right, especially when there’s not much data or the situation is really complex. PEML tries to fix this by bringing in more physical knowledge and ideas from different fields. The paper explains what PEML is and how it can be used to make better decisions in important situations. |
Keywords
» Artificial intelligence » Machine learning
