Summary of Towards Gaussian Process For Operator Learning: An Uncertainty Aware Resolution Independent Operator Learning Algorithm For Computational Mechanics, by Sawan Kumar and Rajdip Nayek and Souvik Chakraborty
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
by Sawan Kumar, Rajdip Nayek, Souvik Chakraborty
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The novel Gaussian Process (GP) based neural operator introduced in this paper is designed to efficiently handle large datasets and provide reliable uncertainty quantification. The approach combines the expressive capability of deterministic neural operators with the uncertainty awareness of conventional GP, leveraging a “neural operator-embedded kernel” that formulates the GP kernel in the latent space learned using a neural operator. A stochastic dual descent (SDD) algorithm is used to train both the neural operator parameters and the GP hyperparameters simultaneously. This addresses traditional GP model limitations, allowing for input-resolution independence and scalability in high-dimensional and non-linear parametric systems like those encountered in computational mechanics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve complex problems in physics using artificial intelligence. It uses a special kind of computer program called a neural operator to help solve equations that describe how things move and change over time. The method is more efficient and accurate than older methods, making it useful for solving big problems like those found in computational mechanics. |
Keywords
» Artificial intelligence » Latent space
