Data for one dimensional sea ice floe modeling

Graph neural network for colliding particles with an application to sea ice floe modeling 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 rendering 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.

面向碰撞粒子的图神经网络及其在海冰浮冰建模中的应用提出了一种基于图神经网络（Graph Neural Networks, GNNs）的海冰建模新方法，该方法充分利用海冰的天然图结构：节点代表单个浮冰，边则对包括碰撞在内的物理相互作用进行建模。该研究以一维框架作为基础，开展该方法的构建工作。传统数值方法虽效果优异，但计算成本高昂且可扩展性较差。本研究提出的模型被命名为碰撞捕获网络（Collision-captured Network, CN），通过引入图神经网络，该模型集成了数据同化（Data Assimilation, DA）技术，能够有效学习并预测不同条件下的海冰动力学过程。该方法通过合成数据（包含存在观测数据点与缺失观测数据点两种场景）进行了验证，结果表明模型在不降低预测精度的前提下，显著加快了轨迹推演速度。这一进展为边缘冰区（Marginal Ice Zones, MIZ）的海冰预报提供了更高效的工具，同时也彰显了将机器学习与数据同化相结合以实现更高效精准建模的潜力。