Surface current prediction based on a physics-informed deep learning model

Abstract

Sea surface current is an important factor that affects various aspects of oceanic climate, energy, and shipping. Traditional methods for simulating current based on ocean dynamic equations are computationally complex, while existing data-driven deep learning methods overlook the constraints of actual physical processes, limiting the further improvement of prediction accuracy. In this paper, we address the issue of sea surface current prediction by proposing a deep learning model that incorporates physical processes. The model combines physical information with deep learning techniques to overcome the limitations of low computational efficiency in numerical models and the lack of interpretability in data-driven deep learning methods. This integration allows us to improve the prediction accuracy of sea surface current. Furthermore, we propose a weighted loss function into the model to address the challenge of accurately predicting high-magnitude current. The research results demonstrate that our proposed prediction model for sea surface current outperforms the baseline model, particularly in predicting high-magnitude current. Simultaneously, the model demonstrates favorable generalization performance and lower data dependency. This paper provides a new and effective method and strategy for sea surface current prediction.