用神经算子实现海洋流速任意分辨率预测与降尺度。
Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
- 基于神经算子构建可任意分辨率求解的物理方程代理模型。
- 在真实Copernicus数据和合成纳维-斯托克斯数据上验证有效。
- 适合需要高精度局部海洋流速分析的研究者使用。
准确建模由偏微分方程（PDE）控制的物理系统是科学计算的核心挑战。在海洋学中，高分辨率流速数据对海岸管理、环境监测和海上安全至关重要。然而，现有卫星产品如Copernicus提供的海流速度数据空间分辨率为约0.08度，全球海洋模型也常缺乏精细局部分析所需的分辨率。本文提出：(a) 一种基于神经算子的监督深度学习框架，用于求解PDE并提供任意分辨率解；(b) 针对Copernicus海洋流速数据的降尺度模型。此外，该方法可建模代理PDE，并在任意分辨率下预测解，不受输入分辨率限制。我们在真实世界Copernicus海洋流速数据和合成纳维-斯托克斯模拟数据集上进行了评估。
原文摘要 · Abstract (English)
Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanography, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。