Summary of From Pinns to Pikans: Recent Advances in Physics-informed Machine Learning, by Juan Diego Toscano et al.
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
by Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou, Nazanin Ahmadi Daryakenari, Chenxi Wu, George Em Karniadakis
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 reviews the latest advancements in Physics-Informed Neural Networks (PINNs), a key tool in Scientific Machine Learning for solving ordinary and partial differential equations using sparse measurements. The authors cover improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. They also survey applications across fields like biomedicine, fluid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. The paper provides a comprehensive overview of PINNs, including Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which offer a promising alternative to traditional PINNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PINNs are a way to solve math problems using neural networks. They’re good at solving problems that involve physics and numbers. Scientists have been working on making PINNs better, and this paper looks at what they’ve learned so far. It talks about new ideas for how to design the networks, how to make them work faster, and how to be sure of the answers. The paper also shows how PINNs are being used in different fields like medicine, water flow, earthquakes, and more. |
Keywords
* Artificial intelligence * Machine learning * Optimization
