Summary of Closure Discovery For Coarse-grained Partial Differential Equations Using Grid-based Reinforcement Learning, by Jan-philipp Von Bassewitz et al.
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning
by Jan-Philipp von Bassewitz, Sebastian Kaltenbach, Petros Koumoutsakos
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); 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 This paper proposes a novel approach to identifying closures in under-resolved Partial Differential Equations (PDEs) using grid-based Reinforcement Learning. The method, which incorporates inductive bias and exploits locality through a Fully Convolutional Network (FCN), is demonstrated through numerical solutions of the advection equation and the Burgers’ equation. The results show accurate predictions for both in-distribution and out-of-distribution test cases, with significant speedup compared to resolving all scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about things that happen at different times and places, like weather forecasts or tracking wildfires. Right now, we have to use simplified models that leave out important details, which can be a problem. The researchers came up with a new way to fill in those gaps using something called Reinforcement Learning. It works by finding patterns in the data and learning from them. They tested this method on two different equations and found it was really good at making accurate predictions, even when they were testing it with new scenarios. This could be useful for all sorts of situations where we need to make predictions about complex systems. |
Keywords
* Artificial intelligence * Convolutional network * Reinforcement learning * Tracking
