This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the simulation of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling.

翻译：本文提出了一种利用图神经网络（GNN）进行海冰建模的新方法，该方法利用海冰的自然图结构，其中节点代表单个冰体，边则模拟包括碰撞在内的物理相互作用。这一概念在一维框架中作为基础步骤进行开发。传统的数值方法虽然有效，但计算量大且可扩展性较差。通过采用图神经网络，所提出的模型——称为碰撞捕捉网络（CN）——结合数据同化（DA）技术，能够有效学习并预测不同条件下的海冰动力学。该方法使用合成数据进行了验证（包括有观测数据点和无观测数据点两种情况），结果表明该模型在不牺牲精度的前提下加速了轨迹模拟。这一进展为边缘冰区（MIZ）的预测提供了更高效的工具，并凸显了机器学习与数据同化相结合以实现更高效建模的潜力。