Summary of Element-wise Multiplication Based Deeper Physics-informed Neural Networks, by Feilong Jiang et al.
Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
by Feilong Jiang, Xiaonan Hou, Min Xia
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 The abstract proposes a new framework, Deeper Physics-Informed Neural Network (Deeper-PINN), to overcome limitations in resolving partial differential equations (PDEs) using Physics-Informed Neural Networks (PINNs). PINNs are restricted by lack of expressive ability and initialization pathology issues. Deeper-PINNs incorporate element-wise multiplication to transform features into high-dimensional spaces, alleviating initialization pathologies and enhancing expressiveness. The framework is tested on various benchmarks, demonstrating improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve complex math problems has been developed. It’s called Deeper Physics-Informed Neural Network (Deeper-PINN). This method helps overcome some limitations in using another method called Physics-Informed Neural Networks (PINNs) for solving these problems. PINNs aren’t good at solving complex problems because they have trouble starting and can be limited in what they can do. The new Deeper-PINNs fix these issues by changing how it looks at the problem. This makes it better at solving complex math problems. |
Keywords
» Artificial intelligence » Neural network
