Summary of Initialization-enhanced Physics-informed Neural Network with Domain Decomposition (idpinn), by Chenhao Si and Ming Yan
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)
by Chenhao Si, Ming Yan
First submitted to arxiv on: 5 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 physics-informed neural network framework, IDPINN, is introduced, which leverages initialization enhancement and domain decomposition to boost prediction accuracy. This architecture trains a Physics-Informed Neural Network (PINN) using a small dataset to obtain an initial network structure, then applies this initialization to each subdomain. Additionally, the smoothness condition on the interface is exploited to improve prediction performance. IDPINN is numerically evaluated on multiple forward problems, showcasing its improved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IDPINN is a new way to make computers better at predicting things by combining two ideas: making neural networks smarter and breaking problems into smaller pieces. It starts by using a small amount of data to create an initial version of the network, then uses this starting point to solve each part of the problem separately. The network also uses information from the edges between these parts to make its predictions even better. IDPINN is tested on several different types of problems and shows that it can be very accurate. |
Keywords
» Artificial intelligence » Neural network
