  Training an effective deep learning model to learn ocean processes involves
careful choices of various hyperparameters. We leverage the advanced search
algorithms for multiobjective optimization in DeepHyper, a scalable
hyperparameter optimization software, to streamline 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.
