DiffSWE2d：用于端到端洪水和海啸建模的可微分浅水方程求解器
DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling
- Earth Sciences New Zealand（地球科学新西兰）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DiffSWE2d是一个基于PyTorch的可微分浅水方程求解器，通过自动微分实现梯度直接传播，用于端到端洪水与海啸建模，并验证了其在反演优化中的有效性。
AI中文摘要：
使用传统浅水方程（SWE）求解器解决反演和优化问题在计算上可能非常昂贵，特别是当需要通过重复的前向模拟来估计相对于模型输入或参数的梯度时。本文介绍了DiffSWE2d，一个基于PyTorch实现的开源可微分浅水方程求解器，用于端到端的洪水和海啸建模。通过利用自动微分，DiffSWE2d将时间推进物理过程表示为可微分的计算图，使梯度能够直接通过数值求解器传播。我们针对两个已建立的基准案例验证了该求解器，并展示了其在海啸波形反演中的应用，证明了其通过基于梯度的优化推断模型输入的能力。DiffSWE2d为将基于物理的水动力建模与现代优化和机器学习方法相结合提供了一个灵活的框架。源代码和可复现示例公开于：此HTTPS URL
英文摘要：
Solving inverse and optimisation problems with traditional shallow water equations (SWE) solvers can be computationally expensive, particularly when gradients with respect to model inputs or parameters must be estimated through repeated forward simulations. In this paper, we introduce DiffSWE2d, an open-source differentiable shallow water equations solver for end-to-end flood and tsunami modelling implemented in PyTorch. By leveraging automatic differentiation, DiffSWE2d represents the time-marching physics as a differentiable computational graph, enabling gradients to be propagated directly through the numerical solver. We validate the solver against two established benchmark cases and demonstrate its application to tsunami waveform inversion, showing its ability to infer model inputs through gradient-based optimisation. DiffSWE2d provides a flexible framework for integrating physics-based hydrodynamic modelling with modern optimisation and machine learning methods. The source code and reproducible examples are publicly available at: https://github.com/ZhonghouXu/DiffSWE2d
