Summary of Sinc Kolmogorov-arnold Network and Its Applications on Physics-informed Neural Networks, by Tianchi Yu et al.
Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed Neural Networks
by Tianchi Yu, Jingwei Qiu, Jiang Yang, Ivan Oseledets
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Numerical Analysis (math.NA); 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 In this paper, researchers explore the use of Sinc interpolation within Kolmogorov-Arnold Networks (KANs), neural networks with learnable activation functions. KANs are gaining attention as alternatives to traditional multilayer perceptrons. By applying Sinc interpolation, the authors demonstrate its effectiveness in representing both smooth and singular functions, a crucial aspect for function approximation and solving partial differential equations using physics-informed neural networks (PINNs). Through experiments, they show that SincKANs consistently outperform other approaches in various examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Using special kinds of math, scientists are trying to create new ways to do calculations. They took something called Kolmogorov-Arnold Networks and added a new trick called Sinc interpolation. This helps them figure out both easy and hard problems better. It’s like having a superpower calculator that can handle tricky math. |
Keywords
* Artificial intelligence * Attention
