Summary of An Evolutionary Approach For Discovering Non-gaussian Stochastic Dynamical Systems Based on Nonlocal Kramers-moyal Formulas, by Yang Li et al.
An evolutionary approach for discovering non-Gaussian stochastic dynamical systems based on nonlocal Kramers-Moyal formulas
by Yang Li, Shengyuan Xu, Jinqiao Duan
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Dynamical Systems (math.DS)
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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 presents an evolutionary symbol sparse regression (ESSR) approach to extract non-Gaussian stochastic dynamical systems from sample path data. The method combines genetic programming, sparse regression, and nonlocal Kramers-Moyal formulas to learn the governing equations of complex systems with both Gaussian Brownian noise and non-Gaussian Lévy noise. The authors demonstrate the effectiveness of this approach through its application to several illustrative models, showcasing its potential for deciphering non-Gaussian stochastic dynamics from available datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to discover the rules that govern complicated systems that have both random noise and special patterns. It uses a new method called ESSR, which combines different techniques like genetic programming, sparse regression, and some special math formulas. This approach can be used to figure out the rules of these complex systems from the data we collect, which has many applications across different fields. |
Keywords
» Artificial intelligence » Regression
