  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.
