Summary of Poseidon: Efficient Foundation Models For Pdes, by Maximilian Herde et al.
Poseidon: Efficient Foundation Models for PDEs
by Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra
First submitted to arxiv on: 29 May 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 Poseidon is a foundation model designed to learn the solution operators of partial differential equations (PDEs). Built on top of a multiscale operator transformer, it incorporates time-conditioned layer norms for continuous-in-time evaluations. A novel training strategy leveraging the semi-group property of time-dependent PDEs enables scaling up training data. Poseidon is pre-trained on a large-scale dataset for fluid dynamics and evaluated on 15 challenging downstream tasks spanning various PDE types and operators. It outperforms baselines in terms of sample efficiency and accuracy, generalizes well to new physics, and scales with model and data size. These results demonstrate Poseidon’s potential as an effective, general-purpose PDE foundation model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Poseidon is a special kind of computer program that can learn from partial differential equations (PDEs). It’s like a super smart student who can understand complex math problems! The program uses a special way to look at the math problems and then tries to solve them. Poseidon was trained on lots of different PDEs and then tested on even more challenging ones. It did really well, solving problems it had never seen before! |
Keywords
» Artificial intelligence » Transformer
