Summary of Interpretable and Efficient Data-driven Discovery and Control Of Distributed Systems, by Florian Wolf et al.
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
by Florian Wolf, Nicolò Botteghi, Urban Fasel, Andrea Manzoni
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Optimization and Control (math.OC)
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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 In this paper, researchers propose a novel framework for controlling systems governed by Partial Differential Equations (PDEs), which are crucial in several fields of Applied Sciences and Engineering. The proposed Dyna-style Model-Based Reinforcement Learning (RL) framework combines the Sparse Identification of Nonlinear Dynamics with Control (SINDy-C) algorithm and an autoencoder (AE) framework for dimensionality reduction of PDE states and actions. This approach enables fast rollouts, reducing the need for extensive environment interactions, and provides an interpretable latent space representation of the PDE forward dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper is about finding new ways to control complex systems that follow rules described by Partial Differential Equations (PDEs). These systems are important in many areas like engineering and science. The researchers are using a type of machine learning called Reinforcement Learning (RL) to help control these systems. They’re making it more efficient, easier to understand, and better at handling complex problems. |
Keywords
» Artificial intelligence » Autoencoder » Dimensionality reduction » Latent space » Machine learning » Reinforcement learning
