Summary of Ups: Efficiently Building Foundation Models For Pde Solving Via Cross-modal Adaptation, by Junhong Shen et al.
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
by Junhong Shen, Tanya Marwah, Ameet Talwalkar
First submitted to arxiv on: 11 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 The Unified PDE Solvers (UPS) approach presents a novel method for developing neural operators that can handle diverse families of spatiotemporal partial differential equations (PDEs) from various domains, dimensions, and resolutions. By embedding different PDEs into a shared representation space and processing them using a FNO-transformer architecture, UPS achieves state-of-the-art results on 1D and 2D PDE families from PDEBench while reducing data and compute requirements by four and twenty-six times, respectively. Additionally, UPS demonstrates few-shot transfer capabilities to unseen PDE families and coefficients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Unified PDE Solvers approach is a new way to solve different types of partial differential equations (PDEs) that happen in many areas like physics, engineering, and computer science. It’s a special kind of artificial intelligence that can learn from small amounts of data and apply what it learned to new problems quickly. This helps us solve PDE problems more efficiently and effectively. |
Keywords
* Artificial intelligence * Embedding * Few shot * Spatiotemporal * Transformer
