DeepAI
Log In Sign Up

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

06/14/2021
by   Niv Giladi, et al.
25

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 downsampling 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.

READ FULL TEXT

page 7

page 8

page 16

page 17

page 18

page 19

01/26/2023

Random Grid Neural Processes for Parametric Partial Differential Equations

We introduce a new class of spatially stochastic physics and data inform...
06/24/2019

Two-Grid based Adaptive Proper Orthogonal Decomposition Algorithm for Time Dependent Partial Differential Equations

In this article, we propose a two-grid based adaptive proper orthogonal ...
12/20/2022

Learning Subgrid-scale Models with Neural Ordinary Differential Equations

We propose a new approach to learning the subgrid-scale model effects wh...
05/31/2020

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

The Physics-Informed Neural Network (PINN) framework introduced recently...
07/24/2020

Convergence analysis of inexact two-grid methods: A theoretical framework

Multigrid methods are among the most efficient iterative techniques for ...
11/23/2022

SciAI4Industry – Solving PDEs for industry-scale problems with deep learning

Solving partial differential equations with deep learning makes it possi...
06/11/2022

PhML-DyR: A Physics-Informed ML framework for Dynamic Reconfiguration in Power Systems

A transformation of the US electricity sector is underway with aggressiv...