Summary of Physics-informed Neural Networks and Extensions, by Maziar Raissi et al.
Physics-Informed Neural Networks and Extensions
by Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi, George Em Karniadakis
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 reviews the rapidly growing field of Physics-Informed Neural Networks (PINNs), which have emerged as a key innovation in scientific machine learning. The authors delve into the practical applications of PINNs, including recent extensions and advancements. Specifically, they demonstrate the potential of PINNs for data-driven discovery of governing differential equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physics-Informed Neural Networks are a game-changer in scientific machine learning! This paper explains how PINNs work and shows how they can be used to find new ways to understand complex systems governed by differential equations. |
Keywords
» Artificial intelligence » Machine learning
