Summary of Learning Physics Informed Neural Odes with Partial Measurements, by Paul Ghanem et al.
Learning Physics Informed Neural ODEs With Partial Measurements
by Paul Ghanem, Ahmet Demirkaya, Tales Imbiriba, Alireza Ramezani, Zachary Danziger, Deniz Erdogmus
First submitted to arxiv on: 11 Dec 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 paper proposes a sequential optimization framework for learning dynamics governing physical and spatiotemporal processes, specifically when parts of the system’s states are not measured. Inspired by state estimation theory and Physics Informed Neural ODEs, the approach learns dynamics governing unmeasured processes. The proposed method is demonstrated through numerical simulations and a real dataset extracted from an electro-mechanical positioning system, showcasing improved performance compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem in physics and engineering. It helps us learn about systems that are only partly measured. This can be hard because we don’t know what’s going on with the parts of the system we’re not measuring. The researchers came up with a new way to solve this problem using techniques from state estimation and neural networks. They tested their approach with computer simulations and real data from an electro-mechanical positioning system, showing that it works better than other methods. |
Keywords
» Artificial intelligence » Optimization » Spatiotemporal
