Prediction of Sea Ice Velocity and Concentration in the Arctic Ocean using Physics-informed Neural Network

• URL: http://arxiv.org/abs/2510.17756v1
• Date: Mon, 20 Oct 2025 17:10:01 GMT
• Title: Prediction of Sea Ice Velocity and Concentration in the Arctic Ocean using Physics-informed Neural Network
• Authors: Younghyun Koo, Maryam Rahnemoonfar,
• Abstract summary: We develop physics-informed neural network (PINN) strategies to integrate physical knowledge of sea ice into the machine learning model.<n>Our PINN model outperforms the fully data-driven model in the daily predictions of sea ice velocity (SIV) and sea ice concentration (SIC)<n>The PINN approach particularly improves SIC predictions in melting and early freezing seasons and near fast-moving ice regions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As an increasing amount of remote sensing data becomes available in the Arctic Ocean, data-driven machine learning (ML) techniques are becoming widely used to predict sea ice velocity (SIV) and sea ice concentration (SIC). However, fully data-driven ML models have limitations in generalizability and physical consistency due to their excessive reliance on the quantity and quality of training data. In particular, as Arctic sea ice entered a new phase with thinner ice and accelerated melting, there is a possibility that an ML model trained with historical sea ice data cannot fully represent the dynamically changing sea ice conditions in the future. In this study, we develop physics-informed neural network (PINN) strategies to integrate physical knowledge of sea ice into the ML model. Based on the Hierarchical Information-sharing U-net (HIS-Unet) architecture, we incorporate the physics loss function and the activation function to produce physically plausible SIV and SIC outputs. Our PINN model outperforms the fully data-driven model in the daily predictions of SIV and SIC, even when trained with a small number of samples. The PINN approach particularly improves SIC predictions in melting and early freezing seasons and near fast-moving ice regions.

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