Summary of Sea: State-exchange Attention For High-fidelity Physics Based Transformers, by Parsa Esmati et al.
SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers
by Parsa Esmati, Amirhossein Dadashzadeh, Vahid Goodarzi, Nicolas Larrosa, Nicolò Grilli
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel transformer-based module, called the State-Exchange Attention (SEA) module, that enables information exchange between encoded fields in dynamical systems. The SEA module is designed to capture physical relationships and symmetries between fields through multi-head cross-attention. The authors also introduce an efficient ViT-like mesh autoencoder for generating spatially coherent mesh embeddings. Experimental results show that the SEA-integrated transformer outperforms competitive baselines, with a reduction in error of 88% and 91%, respectively. The paper demonstrates the state-of-the-art rollout error compared to other approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict future states of complex systems using dynamical equations. They created a special kind of artificial intelligence called the State-Exchange Attention (SEA) module, which helps different parts of the system talk to each other and understand their relationships. This approach is better than others because it reduces errors by 88% and 91%, making it more accurate for predicting future states. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Cross attention » Transformer » Vit
