Abstract
A data-driven emulator for the baroclinic double gyre ocean simulation
is presented in this study. Traditional numerical simulations using
partial differential equations (PDEs) often require substantial
computational resources, hindering real-time applications and inhibiting
model scalability. This study presents a novel approach employing neural
operators to address these challenges in an idealized double-gyre ocean
simulation. We propose a deep learning approach capable of learning the
underlying dynamics of the ocean system, complementing the classical
methods, and effectively replacing the need for explicit PDE solvers at
inference time. By leveraging neural operators, we efficiently integrate
the governing equations, providing a data-driven and computationally
efficient framework for simulating the double-gyre ocean circulation.
Our approach demonstrates promising results in terms of accuracy and
computational efficiency, showcasing the potential for advancing ocean
modeling through the fusion of neural operators and traditional
oceanographic methodologies. In comparison to a dynamical numerical
model, we obtain 600x speedups allowing us to create 2000-day ensembles
in tens of seconds instead of hours.