Although numerical models provide accurate solutions for ice sheet dynamics based on physics laws, they accompany intensified computational demands to solve partial differential equations. In recent years, convolutional neural networks (CNNs) have been widely used as statistical emulators for those numerical models. However, since CNNs operate on regular grids, they cannot represent the refined meshes and computational efficiency of finite-element numerical models. Therefore, instead of CNNs, this study adopts an equivariant graph convolutional network (EGCN) as an emulator for the ice sheet dynamics modeling. EGCN reproduces ice thickness and velocity changes in the Helheim Glacier, Greenland, and Pine Island Glacier, Antarctica, with 260 times and 44 times faster computation time, respectively. Compared to the traditional CNN and graph convolutional network, EGCN shows outstanding accuracy in thickness prediction near fast ice streams by preserving the equivariance to the translation and rotation of graphs.

翻译：尽管数值模型基于物理定律为冰盖动力学提供了精确解，但其求解偏微分方程伴随显著的计算负担。近年来，卷积神经网络（CNN）已被广泛用作此类数值模型的统计模拟器。然而，由于CNN在规则网格上运行，其无法体现有限元数值模型的精细化网格与计算效率。因此，本研究采用等变图卷积网络（EGCN）替代CNN，作为冰盖动力学建模的模拟器。EGCN在格陵兰海勒姆冰川和南极松岛冰川的冰厚度与流速变化模拟中，分别实现了260倍与44倍的计算加速。相较于传统CNN与图卷积网络，EGCN通过保持对图平移与旋转的等变性，在快速冰流附近的厚度预测中展现出卓越的准确性。