Graph Neural Network as Computationally Efficient Emulator of Ice-sheet and Sea-level System Model (ISSM)
This provides a computationally efficient method for predicting ice sheet dynamics, such as future changes in the Pine Island Glacier under different melting scenarios, though it is incremental as it adapts existing GCN techniques to a specific domain.
The authors tackled the computational inefficiency of the Ice-sheet and Sea-level System Model (ISSM) by designing a graph convolutional network (GCN) as a fast emulator, achieving a correlation coefficient greater than 0.998 for ice thickness and velocity and a 34 times speedup compared to the CPU-based ISSM.
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.