A Neural Operator-Based Emulator for Regional Shallow Water Dynamics

• URL: http://arxiv.org/abs/2502.14782v1
• Date: Thu, 20 Feb 2025 18:02:44 GMT
• Title: A Neural Operator-Based Emulator for Regional Shallow Water Dynamics
• Authors: Peter Rivera-Casillas, Sourav Dutta, Shukai Cai, Mark Loveland, Kamaljyoti Nath, Khemraj Shukla, Corey Trahan, Jonghyun Lee, Matthew Farthing, Clint Dawson,
• Abstract summary: Coastal regions are particularly vulnerable to the impacts of rising sea levels and extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. We present a novel autoregressive neural emulator that employs dimensionality reduction to efficiently approximate high-dimensional numerical solvers.
• Score: 5.09419041446345
• License:
• Abstract: Coastal regions are particularly vulnerable to the impacts of rising sea levels and extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. In this study, we present the Multiple-Input Temporal Operator Network (MITONet), a novel autoregressive neural emulator that employs dimensionality reduction to efficiently approximate high-dimensional numerical solvers for complex, nonlinear problems that are governed by time-dependent, parameterized partial differential equations. Although MITONet is applicable to a wide range of problems, we showcase its capabilities by forecasting regional tide-driven dynamics described by the two-dimensional shallow-water equations, while incorporating initial conditions, boundary conditions, and a varying domain parameter. We demonstrate MITONet's performance in a real-world application, highlighting its ability to make accurate predictions by extrapolating both in time and parametric space.

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