Summary of Large Scale Scattering Using Fast Solvers Based on Neural Operators, by Zongren Zou et al.
Large scale scattering using fast solvers based on neural operators
by Zongren Zou, Adar Kahana, Enrui Zhang, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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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 extends the hybrid iterative transferable solver (HINTS) to solve the scattering problem described by the Helmholtz equation in an exterior domain with a complex absorbing boundary condition. The HINTS method combines neural operators (NOs) with standard iterative solvers, such as Jacobi and Gauss-Seidel (GS), to achieve better performance by leveraging the spectral bias of neural networks. The authors apply HINTS to solve scattering problems for both 2D and 3D geometries, including square and triangular scatterers in 2D, and a cube and a model submarine in 3D. They demonstrate the extrapolation capability of HINTS in handling diverse geometries of the scatterer, without requiring retraining or fine-tuning the neural operator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a machine-learning-based approach to solve scattering problems described by the Helmholtz equation. The authors extend a previous method called HINTS (hybrid iterative transferable solver) to handle more complex problems. They use a combination of neural networks and standard iterative solvers to make predictions. The results show that this method can accurately solve scattering problems for different shapes and sizes, without needing to retrain the model each time. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning
