Summary of A Physics Informed Neural Network (pinn) Methodology For Coupled Moving Boundary Pdes, by Shivprasad Kathane and Shyamprasad Karagadde (indian Institute Of Technology Bombay Mumbai India)
A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
by Shivprasad Kathane, Shyamprasad Karagadde
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Analysis of PDEs (math.AP)
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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 multi-task learning framework, Physics-Informed Neural Network (PINN), integrates knowledge of physics and known constraints into deep learning components to solve physical problems modeled using differential equations. PINNs have been applied to uncoupled systems, but this work reports a PINN-based approach for solving coupled systems involving multiple governing parameters and interface balance equations. The methodology employs separate networks for each variable, adaptive loss weighting, and progressive optimization space reduction. This is demonstrated by solving the benchmark problem of binary alloy solidification, which captures complex composition profiles and aligns with analytical solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A Physics-Informed Neural Network (PINN) is a new way to solve problems that involve physical laws and rules. It’s like a special kind of computer program that can understand how things move and change over time. This approach has been tested on some simple problems, but this study shows it can also be used for more complex problems that involve multiple variables and rules. The results are promising, with the PINN being able to accurately predict the behavior of a binary alloy as it solidifies. |
Keywords
» Artificial intelligence » Deep learning » Multi task » Neural network » Optimization
