Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications.

深度学习模型框架在预测时间序列数据方面具有潜在应用，尤其是在大气和海洋领域。本研究探讨了利用深度神经操作模型对经典流体流动和真实海洋动力学进行重现和预测的可能性，并证实了这些模型对理想化周期性涡流脱落和真实海洋表面流动的一些特征的预测能力。

深度神经算子模型在海洋预测中的评估