An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling

The paper introduces an Adaptive‑Horizon Graph Neural Operator (AH‑GNO) that learns to predict bed elevation changes directly on unstructured meshes by integrating area‑weighted graph learning, historical state selection, and forecast horizon estimation. Evaluated on Telemac2D/GAIA benchmarks and a field‑scale Waiho River case, the AH‑GNO accurately reproduces key morphodynamic patterns while drastically reducing bed‑update computation time. Deployment on CPU‑GPU platforms achieves speedups of 150–360× over 4‑core GAIA CPUs and 105–186× over 20‑core CPUs.

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