Summary of Neural Operators with Localized Integral and Differential Kernels, by Miguel Liu-schiaffini et al.
Neural Operators with Localized Integral and Differential Kernels
by Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
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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 approach to learning differential and integral operators is proposed, enabling the capture of local features while maintaining the ability to predict at any resolution. This is achieved by scaling kernel values of convolutional neural networks (CNNs) to obtain differential operators, or using discrete-continuous convolutions for local integral operators. The resulting models outperform traditional Fourier neural operators (FNOs), reducing relative L2-error by 34-72% in experiments with turbulent Navier-Stokes and shallow water equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have created new ways to learn from complex problems, like understanding weather patterns or ocean currents. They’ve developed special types of artificial intelligence that can look at data from different angles and make predictions about what might happen next. This helps them better understand the world around us and make more accurate forecasts. |
