Summary of Learning Macroscopic Dynamics From Partial Microscopic Observations, by Mengyi Chen et al.
Learning Macroscopic Dynamics from Partial Microscopic Observations
by Mengyi Chen, Qianxiao Li
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Computational Physics (physics.comp-ph)
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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 A novel machine learning approach is proposed to learn macroscopic dynamics requiring only partial force computations on microscopic coordinates. The method relies on a sparsity assumption that forces on each coordinate rely only on a small number of other coordinates. This allows for computationally efficient learning of macroscopic closure models from various microscopic systems, including those modeled by partial differential equations or molecular dynamics simulations. The proposed approach is theoretically justified under suitable conditions and demonstrated to be accurate, force computation efficient, and robust in learning macroscopic dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand big systems has been discovered! Right now, scientists have to do lots of tiny calculations to figure out what’s happening at a bigger scale. But this can take forever for really complex systems. This paper shows how to get around that by just looking at some of the small things and making educated guesses about the rest. It’s like trying to guess what a big picture looks like from just a few small pieces. The idea is tested on lots of different kinds of systems, and it seems to work really well! |
Keywords
* Artificial intelligence * Machine learning
