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- Niv Giladi Google Research and Technion - Israel Institute of Technology
Google Research and Technion - Israel Institute of Technology
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- Zvika Ben-Haim Google Research
Google Research
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- Sella Nevo Google Research
Google Research
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- Yossi Matias Google Research
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- Daniel Soudry Technion - Israel Institute of Technology
Technion - Israel Institute of Technology
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NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsDecember 2021Article No.: 106Pages 1378–1389
Published:10 June 2024Publication History
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NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems
Physics-aware downsampling with deep learning for scalable flood modeling
Pages 1378–1389
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ABSTRACT
Background. Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions. Flood forecasting is therefore a vitally important endeavor, typically achieved using physical water flow simulations, which rely on accurate terrain elevation maps. However, such simulations, based on solving partial differential equations, are computationally prohibitive on a large scale. This scalability issue is commonly alleviated using a coarse grid representation of the elevation map, though this representation may distort crucial terrain details, leading to significant inaccuracies in the simulation.
Contributions. We train a deep neural network to perform physics-informed down-sampling of the terrain map: we optimize the coarse grid representation of the terrain maps, so that the flood prediction will match the fine grid solution. For the learning process to succeed, we configure a dataset specifically for this task. We demonstrate that with this method, it is possible to achieve a significant reduction in computational cost, while maintaining an accurate solution. A reference implementation accompanies the paper as well as documentation and code for dataset reproduction.
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NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems
December 2021
30517 pages
ISBN:9781713845393
- Editors:
- M. Ranzato,
- A. Beygelzimer,
- Y. Dauphin,
- P.S. Liang,
- J. Wortman Vaughan
Copyright © 2021 Neural Information Processing Systems Foundation, Inc.
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Curran Associates Inc.
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- Published: 10 June 2024
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