Physics-Informed Neural Networks for Modeling Ocean Dynamics and Parameter Estimation: Leveraging Ocean Reanalysis Data

• Published in: IEEE journal of oceanic engineering Vol. 50; no. 3; pp. 2248 - 2260
• Main Authors: Hu, Shuang, Liu, Meiqin, Zhang, Senlin, Dong, Shanling, Zheng, Ronghao
• Format: Journal Article
• Language: English
• Published: IEEE 01.07.2025
• Subjects: Accuracy; Boundary conditions; Characteristic-based split (CBS) algorithm; Computational modeling; Data models; Friction; Mathematical models; Neural networks; ocean reanalysis; Oceans; parameter estimation; physics-informed neural network (PINN); Predictive models; shallow-water equation; Tides
• ISSN: 0364-9059; 1558-1691
• DOI: 10.1109/JOE.2025.3538927

Advancements in ocean reanalysis and satellite remote sensing products have opened unprecedented opportunities for using large-scale data sets to analyze and model ocean dynamics. This article utilizes the China Ocean Reanalysis Second Edition (CORA2) data set to model and estimate parameters for the ocean dynamics off the East Coast of China. A novel approach combining physics-informed neural networks with characteristic-based split is innovatively proposed to effectively analyze dynamics issues, such as surface waves and tides under open boundary conditions. This method estimates the boundary amplitude of incoming waves using multiple time-series flow field data from coastal areas in China, and uses these estimates to predict future flow field changes. By comparing with the CORA2 data set, the method not only confirms its high accuracy and reliability but also significantly improves the alignment between model predictions and actual observational data by incorporating estimates of seabed friction coefficients. This reveals the effectiveness of using large-scale data sets in conjunction with physical equations to enhance the accuracy and computational precision of ocean dynamics modeling.