While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for ocean circulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill by outperforming SSH predictions by an uncoupled, state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate the potential of physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.

翻译：尽管数据驱动方法在大气建模和天气预报中展现出巨大潜力，但海洋建模因复杂的地形、陆地边界、垂向结构及流动非线性而面临独特挑战。本研究提出了OceanNet——一种基于神经算子原理的海洋环流数字孪生系统。OceanNet采用傅里叶神经算子与预测-评估-校正积分方案，以缓解自回归误差增长并增强长时间尺度下的稳定性。谱正则化器则用于抑制小尺度上的谱偏差。该系统应用于西北大西洋西边界流（墨西哥湾流），重点关注环流涡旋与湾流蜿蜒的季节性预测任务。通过历史海面高度数据训练，OceanNet展现出卓越的预报能力：其海面高度预测性能优于非耦合的先进动力海洋模型预报，同时计算量降低50万倍。这些成果证明了受物理启发的深度神经算子作为高分辨率数值海洋模型的高效替代方案的潜力。