Summary of Pretraining Codomain Attention Neural Operators For Solving Multiphysics Pdes, by Md Ashiqur Rahman et al.
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
by Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
First submitted to arxiv on: 19 Mar 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 This paper proposes a novel neural operator architecture called Codomain Attention Neural Operator (CoDA-NO) for solving multiphysics problems involving coupled partial differential equations (PDEs). The architecture tokenizes functions along the codomain or channel space, enabling self-supervised learning or pretraining of multiple PDE systems. CoDA-NO extends standard neural operator components like positional encoding, self-attention, and normalization layers to function spaces, allowing it to learn representations of different PDE systems with a single model. The authors evaluate CoDA-NO’s potential as a backbone for learning multiphysics PDEs over multiple systems through few-shot learning settings. They demonstrate that CoDA-NO outperforms existing methods by over 36% on complex downstream tasks such as fluid flow simulations, fluid-structure interactions, and Rayleigh-Bénard convection with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoDA-NO is a new way to solve complicated problems involving different physical systems. This problem is important because it’s hard for computers to understand how these systems interact. The authors came up with an idea called CoDA-NO that can learn about multiple physical systems at the same time. They tested this idea and found that it worked better than other methods in certain situations. |
Keywords
* Artificial intelligence * Attention * Few shot * Positional encoding * Pretraining * Self attention * Self supervised
