Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS), which usually runs on an HPC cluster with multiple CPU cores. However, the process is time-consuming and energy expensive. While coarse-grained ROMS simulations offer faster alternatives, they sacrifice detail and accuracy, particularly in complex coastal environments. Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days). Our approach not only accelerates simulations but also incorporates a physics-based constraint to detect and correct inaccurate results, ensuring reliability while minimizing manual intervention. We develop a fully GPU-accelerated workflow, optimizing the model training and inference pipeline on NVIDIA DGX-2 A100 GPUs. Our experiments demonstrate that our AI surrogate reduces the time cost of 12-day forecasting of traditional ROMS simulations from 9,908 seconds (on 512 CPU cores) to 22 seconds (on one A100 GPU), achieving over 450$\times$ speedup while maintaining high-quality simulation results. This work contributes to oceanographic modeling by offering a fast, accurate, and physically consistent alternative to traditional simulation models, particularly for real-time forecasting in rapid disaster response.

翻译：全球近9亿人口居住在低洼沿海地区，承受着日益频繁且严重的飓风与风暴潮冲击。海洋学家通过模拟沿海洋流环流来构建早期预警系统，以挽救生命并减少海岸灾害造成的财产损失。传统上，此类模拟通常采用海岸海洋环流模型（如区域海洋模型系统ROMS），并在配备多CPU核心的高性能计算集群上运行。然而，该过程耗时耗能巨大。虽然粗粒度ROMS模拟能提供更快的替代方案，但会牺牲细节与精度，在复杂海岸环境中尤为明显。深度学习与GPU架构的最新进展使得开发更快速的人工智能（神经网络）替代模型成为可能。本文提出一种基于4D Swin Transformer的AI替代模型，用于模拟河口区域海岸潮波传播的後报与预报（最长12天）。我们的方法不仅加速了模拟过程，还引入基于物理的约束机制来检测并修正不准确结果，在确保可靠性的同时最大限度减少人工干预。我们开发了完全GPU加速的工作流程，在NVIDIA DGX-2 A100 GPU上优化了模型训练与推理管线。实验表明，我们的AI替代模型将传统ROMS模拟的12天预报时间成本从9,908秒（512个CPU核心）缩减至22秒（单块A100 GPU），实现超过450倍的加速比，同时保持高质量的模拟结果。本研究为海洋学建模提供了快速、精确且物理一致的传统模拟模型替代方案，尤其适用于快速灾害响应中的实时预报场景。