The Ice-sheet and Sea-level System Model (ISSM) provides solutions for Stokes equations relevant to ice sheet dynamics by employing finite element and fine mesh adaption. However, since its finite element method is compatible only with Central Processing Units (CPU), the ISSM has limits on further economizing computational time. Thus, by taking advantage of Graphics Processing Units (GPUs), we design a graph convolutional network (GCN) as a fast emulator for ISSM. The GCN is trained and tested using the 20-year transient ISSM simulations in the Pine Island Glacier (PIG). The GCN reproduces ice thickness and velocity with a correlation coefficient greater than 0.998, outperforming the traditional convolutional neural network (CNN). Additionally, GCN shows 34 times faster computational speed than the CPU-based ISSM modeling. The GPU-based GCN emulator allows us to predict how the PIG will change in the future under different melting rate scenarios with high fidelity and much faster computational time.

利用图形处理单元设计了一个图卷积网络 (GCN) 作为 ISSM 的快速仿真器，它在模拟冰川动力学时具有比传统卷积神经网络 (CNN) 更高的重复冰厚度和速度的相关性系数，同时计算速度比基于 CPU 的 ISSM 模型快约 34 倍，该基于 GPU 的 GCN 仿真器能够快速、高保真度地预测 Pine Island Glacier 在不同融化速率情景下的未来变化。

图神经网络作为冰盖和海平面系统模型的计算高效仿真器 (ISSM)