English(EN) Operator Learning for Predicting Bulk Wave Parameters of Spectral Wave Models

深度算子网络加速波浪预测模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种深度算子网络（DeepONet）作为预测体波参数的代理模型，旨在降低风暴潮预测的计算成本。该代理模型学习底层的连续算子，能够实现独立于离散化的有效预测。在北卡罗来纳州鸭子进行的真实模拟测试中，DeepONet在计算效率上实现了四个数量级的提升，同时在预测有效波高和辐射应力梯度方面保持了高精度。 AI

影响这项研究通过创建更高效的复杂波浪模拟代理模型，有可能显著加速风暴潮预测。

排序理由这是一篇详细介绍波浪预测新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shukai Cai, Sourav Dutta, Mark Loveland, Eirik Valseth, Peter Rivera-Casillas, Corey Trahan, Clint Dawson · 2026-09-01 04:00

用于预测谱波模型体波参数的算子学习

arXiv:2604.06433v2 Announce Type: replace-cross Abstract: The impact of wave-induced forcing on the mean water level and nearshore currents is typically modeled through excess momentum fluxes, also known as radiation stresses, and their spatial gradients. Accurate storm surge pre…

本文如何被排名

报道来源 [1]

用于预测谱波模型体波参数的算子学习

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