Summary of Symmetry-informed Governing Equation Discovery, by Jianke Yang et al.
Symmetry-Informed Governing Equation Discovery
by Jianke Yang, Wang Rao, Nima Dehmamy, Robin Walters, Rose Yu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed method in this paper aims to improve the accuracy and simplicity of learning governing differential equations from observations of dynamical systems. By leveraging symmetry, specifically time-independent symmetries of ODEs, the approach compresses the equation search space and achieves better results than baseline methods without symmetry. The technique is demonstrated across diverse dynamical systems, showing improved robustness against noise and higher probability of recovering accurate governing equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to learn differential equations from data. Instead of just using any equation that fits the data, this method takes into account important physical laws like symmetry. This helps find simpler and more accurate equations. The method is tested on different systems and shows it can handle noisy data better than other methods. |
Keywords
* Artificial intelligence * Probability
