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arXiv Machine Learning By Thomas Gregov, Sebastian Rosier, Brandon Finley, Andreas Vieli, Guillaume Jouvet

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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