Summary of Dpot: Auto-regressive Denoising Operator Transformer For Large-scale Pde Pre-training, by Zhongkai Hao et al.
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
by Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a new auto-regressive denoising pre-training strategy for training neural operators in data-scarce settings, particularly suitable for partial differential equations (PDEs) data. The approach leverages Fourier attention to design a flexible and scalable model architecture that can be easily scaled up for large-scale pre-training. By training the PDE foundation model with up to 0.5 billion parameters on more than 100,000 trajectories across 10+ PDE datasets, the authors achieve state-of-the-art (SOTA) performance on these benchmarks and demonstrate strong generalizability to diverse downstream PDE tasks, including 3D data. The pre-trained model is available at https://github.com/thu-ml/DPOT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to make computers better at solving math problems. It’s like teaching a computer to solve puzzles! The researchers created a new way to train the computer using lots of different types of math problems, which makes it really good at solving those kinds of problems. They also made sure their method can be used with really big sets of data, so computers can learn from even more examples. This is important because it means computers can get better and better at solving math problems, which will help us make new discoveries. |
Keywords
* Artificial intelligence * Attention
