Summary of Adaptive Interface-pinns (adai-pinns): An Efficient Physics-informed Neural Networks Framework For Interface Problems, by Sumanta Roy et al.
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
by Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Antareep Kumar Sarma
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel framework, Adaptive Interface-PINNs (AdaI-PINNs), is proposed to improve the modeling of interface problems with discontinuous coefficients and/or interfacial jumps. Building upon previous work on Interface PINNs (I-PINNs), AdaI-PINNs employ domain decomposition and adaptive activation functions that vary solely in their slopes, trained alongside other neural network parameters. This fully automated approach reduces computational costs by 2-6 times while achieving similar or better accuracy compared to I-PINNs. The framework is demonstrated on one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve a type of math problem is presented. Called AdaI-PINNs, it’s an improvement over previous methods that can be tricky when dealing with sudden changes in values across boundaries. The new approach is more efficient, using less computer power while getting similar or better results. This was tested on some examples and showed promising results. |
Keywords
* Artificial intelligence * Neural network
