Summary of A Fast Ai Surrogate For Coastal Ocean Circulation Models, by Zelin Xu et al.
A Fast AI Surrogate for Coastal Ocean Circulation Models
by Zelin Xu, Jie Ren, Yupu Zhang, Jose Maria Gonzalez Ondina, Maitane Olabarrieta, Tingsong Xiao, Wenchong He, Zibo Liu, Shigang Chen, Kaleb Smith, Zhe Jiang
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 paper introduces an AI surrogate based on the 4D Swin Transformer to simulate coastal tidal wave propagation in estuaries. The model is designed for both hindcast and forecast applications up to 12 days. The authors develop a fully GPU-accelerated workflow, optimizing the model training and inference pipeline on NVIDIA DGX-2 A100 GPUs. This approach not only accelerates simulations but also incorporates physics-based constraints to detect and correct inaccurate results, ensuring reliability while minimizing manual intervention. The paper demonstrates a significant speedup of over 450 times compared to traditional ROMS simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to predict ocean tides using artificial intelligence (AI). They used a special type of AI called the Swin Transformer to simulate how tidal waves move in coastal areas. This method is much faster than the old way, which uses many computer cores. The AI model also makes sure the predictions are accurate by checking against real data. The new method can forecast tidal waves up to 12 days in advance and could help people prepare for big storms. |
Keywords
» Artificial intelligence » Inference » Transformer
