Summary of Amortized Equation Discovery in Hybrid Dynamical Systems, by Yongtuo Liu et al.
Amortized Equation Discovery in Hybrid Dynamical Systems
by Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Symbolic Computation (cs.SC)
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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 a novel approach to learning the laws governing complex systems with both continuous and discrete states, known as hybrid dynamical systems. The proposed Amortized Equation Discovery (AMORE) framework is an end-to-end method that jointly categorizes modes and discovers equations characterizing the dynamics of each mode by all segments of the mode. This is in contrast to previous methods which follow a two-stage paradigm, grouping time series into small cluster fragments and then discovering equations in each fragment separately. The AMORE framework outperforms previous methods on equation discovery, segmentation, and forecasting in experiments conducted on both hybrid and non-hybrid systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand complex systems that have different states at different times. Usually, we first group these time series into smaller groups and then find the rules that govern each group separately. But this approach doesn’t take advantage of the similarities between the dynamics of multiple groups that are controlled by the same rules. The authors propose a new way to learn about these systems by finding both the categories and the rules at the same time. They test their method on several examples and show that it is better than previous methods at discovering the rules, grouping the data, and making predictions. |
Keywords
» Artificial intelligence » Time series
