Summary of Sparse Identification Of Nonlinear Dynamics in the Presence Of Library and System Uncertainty, by Andrew O’brien
Sparse identification of nonlinear dynamics in the presence of library and system uncertainty
by Andrew O’Brien
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 SINDy algorithm has been widely used for identifying governing equations of complex systems from time series data. However, this approach relies on prior knowledge of the system’s variables and a function library. The Augmented SINDy algorithm is proposed to overcome these limitations by handling uncertainty in both system variables and function libraries. This paper demonstrates the effectiveness of the Augmented SINDy algorithm on real-world datasets, outperforming traditional SINDy methods when dealing with uncertain system variables. Furthermore, it shows that further augmentations can be made to enable robust performance even when both types of uncertainty are present. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SINDy algorithm helps scientists understand complex systems by finding the rules that govern their behavior. However, this method has some limitations. One issue is that it assumes you know which variables are important in the system and have a list of possible functions to use as a starting point. Researchers developed an improved version called Augmented SINDy, which can handle uncertainty in both these areas. In this study, they tested this new algorithm on real-world data and showed it performs better than the original SINDy method when dealing with unknown variables. They also explored further improvements that make the algorithm more robust. |
Keywords
* Artificial intelligence * Time series
