Physics-aware downsampling with deep learning for scalable flood modeling | Proceedings of the 35th International Conference on Neural Information Processing Systems (2024)

Physics-aware downsampling with deep learning for scalable flood modeling | Proceedings of the 35th International Conference on Neural Information Processing Systems (2)

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  • Niv Giladi Google Research and Technion - Israel Institute of Technology

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  • Zvika Ben-Haim Google Research

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  • Sella Nevo Google Research

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  • Yossi Matias Google Research

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  • Daniel Soudry 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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Physics-aware downsampling with deep learning for scalable flood modeling | Proceedings of the 35th International Conference on Neural Information Processing Systems (3)

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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      Physics-aware downsampling with deep learning for scalable flood modeling | Proceedings of the 35th International Conference on Neural Information Processing Systems (62)

      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

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