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Developing fast and accurate surrogates for physics-based coastal and oceanmodels is an urgent need due to the coastal flood risk under accelerating sealevel rise, and the computational expense of deterministic numerical models.For this purpose, we develop the first digital twin of Earth coastlines withnew physics-informed machine learning techniques extending the state-of-artNeural Operator. As a proof-of-concept study, we built Fourier Neural Operator(FNO) surrogates on the simulations of an industry-standard flood and oceanmodel (NEMO). The resulting FNO surrogate accurately predicts the sea surfaceheight in most regions while achieving upwards of 45x acceleration of NEMO. Wedelivered an open-source \textit{CoastalTwin} platform in an end-to-end andmodular way, to enable easy extensions to other simulations and ML-basedsurrogate methods. Our results and deliverable provide a promising approach tomassively accelerate coastal dynamics simulators, which can enable scientiststo efficiently execute many simulations for decision-making, uncertaintyquantification, and other research activities.

Digital Twin Earth -- Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators