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