Summary of Picl: Physics Informed Contrastive Learning For Partial Differential Equations, by Cooper Lorsung and Amir Barati Farimani
PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by Cooper Lorsung, Amir Barati Farimani
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); 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 In this paper, researchers develop a novel approach to improve neural operator generalization across multiple governing equations simultaneously. Neural operators are used as Partial Differential Equation (PDE) surrogate models, learning solution functionals rather than functions. The authors propose a contrastive pretraining framework utilizing Generalized Contrastive Loss, which incorporates physics-informed system evolution and latent-space model output anchored to input data. This approach improves accuracy for the Fourier Neural Operator in fixed-future and autoregressive rollout tasks for various PDEs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how neural operators can be used to solve complex Partial Differential Equations (PDEs). Neural operators are a type of artificial intelligence that can quickly and accurately calculate solutions to PDEs. The authors of this study wanted to see if they could improve the performance of these neural operators by training them on multiple PDEs at once. To do this, they developed a new way of pretraining the neural operators using something called Generalized Contrastive Loss. This approach helps the neural operators learn how to generalize better across different PDEs. The authors tested their method on several different PDEs and found that it improved the accuracy of the results. |
Keywords
* Artificial intelligence * Autoregressive * Contrastive loss * Generalization * Latent space * Pretraining
