Summary of Online Physics-informed Dynamic Mode Decomposition: Theory and Applications, by Biqi Chen and Ying Wang
Online Physics-Informed Dynamic Mode Decomposition: Theory and Applications
by Biqi Chen, Ying Wang
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Adaptation and Self-Organizing Systems (nlin.AO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel adaptation of Dynamic Mode Decomposition (DMD) is presented in this paper, which addresses challenges in computational efficiency, noise sensitivity, and adherence to physical laws. The authors propose Online Physics-informed DMD (OPIDMD), a convex optimization framework that ensures convergence to a unique global optimum and enhances the efficiency and accuracy of modeling dynamical systems in an online setting. This approach is compared to existing algorithms such as Exact DMD, Online DMD, and piDMD, achieving the best prediction performance in short-term forecasting with an R^2 value of 0.991 for noisy Lorenz system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze complex systems called Online Physics-informed DMD (OPIDMD). It’s a better version of another method called Dynamic Mode Decomposition (DMD) that can be used to understand and predict how complex systems change over time. The new approach is faster, more accurate, and works better with noisy data. This means it can be used to solve problems in many fields, such as weather forecasting or controlling robots. |
Keywords
» Artificial intelligence » Optimization
