Summary of Machine Learning with Physics Knowledge For Prediction: a Survey, by Joe Watson et al.
Machine Learning with Physics Knowledge for Prediction: A Survey
by Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An T. Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles Cranmer, Carlo D’Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffman
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Computational Physics (physics.comp-ph)
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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 survey explores the intersection of machine learning and physics, specifically focusing on partial differential equations. The paper examines various methods and models that combine ML with physics knowledge to improve predictive models. It highlights the potential impact on scientific research and industrial practices by enhancing model accuracy using small- or large-scale datasets and incorporating inductive biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey looks at ways to combine machine learning (ML) with physics knowledge for better predictions and forecasts, especially for partial differential equations (PDEs). The methods can help improve models using small or big data sets. The paper talks about two main approaches: one is to add physics knowledge directly into the ML model, and the other is to use data as a way to learn more about the physics. |
Keywords
» Artificial intelligence » Machine learning
