Physics-enriched neural solvers for transient ice-flow simulation
Read the original on arXiv Machine Learning →Physics-enriched neural solvers for transient ice-flow simulation present a method where a neural network represents the glacier velocity field, warm-started from the previous time step and updated with few optimizer iterations. By feeding the network inexpensive input fields derived from low-order ice-flow balances, the solver improves robustness and accuracy across three real-world glacier configurations, achieving surface-velocity errors reduced by factors of two to four at fixed runtime. The approach enables a 300-year Aletsch simulation to finish in under one minute on a single GPU, demonstrating significant computational savings compared to traditional higher-order models.
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