Summary of Finite Basis Kolmogorov-arnold Networks: Domain Decomposition For Data-driven and Physics-informed Problems, by Amanda A. Howard et al.
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
by Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 domain decomposition method for Kolmogorov-Arnold networks (KANs), which are an alternative to multilayer perceptrons (MLPs) in scientific machine learning. The authors aim to reduce the computational cost of training KANs by dividing them into smaller sub-networks that can be trained in parallel. Inspired by finite basis physics-informed neural networks (FBPINNs), they develop a method called finite basis KANs (FBKANs). The proposed approach is tested on multiscale problems with noisy data, demonstrating its effectiveness for physics-informed training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to train computers using something called Kolmogorov-Arnold networks. These networks are good for scientific machine learning, but they can be slow and expensive to use. The authors came up with an idea to break these networks into smaller pieces that can be trained separately. This makes it faster and cheaper to get accurate results from complex problems. |
Keywords
» Artificial intelligence » Machine learning
