Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper's advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNOs in ocean dynamics forecasting, offering a scalable solution with improved precision.

利用DeepHyper的高级搜索算法优化深度学习模型的超参数选择，尤其针对海洋模拟进行数据驱动建模，以提高模型精确性。实验结果显示，优化后的超参数集在单时间步预测中改善了模型性能，并在长时间范围内的自回归预测中大幅超过了基线配置。利用DeepHyper展示了在海洋动力学预测中提升FNOs使用的可扩展解决方案。

用傅立叶神经算子优化多目标超参数和架构的洋流动力学建模