OceanNet: A principled neural operator-based digital twin for regional
  oceans
Ashesh Chattopadhyay Michael Gray Tianning Wu Anna B. Lowe Ruoying He
While data-driven approaches demonstrate great potential in atmospheric
modeling and weather forecasting, ocean modeling poses distinct challenges due
to complex bathymetry, land, vertical structure, and flow non-linearity. This
study introduces OceanNet, a principled neural operator-based digital twin for
ocean circulation. OceanNet uses a Fourier neural operator and
predictor-evaluate-corrector integration scheme to mitigate autoregressive
error growth and enhance stability over extended time scales. A spectral
regularizer counteracts spectral bias at smaller scales. OceanNet is applied to
the northwest Atlantic Ocean western boundary current (the Gulf Stream),
focusing on the task of seasonal prediction for Loop Current eddies and the
Gulf Stream meander. Trained using historical sea surface height (SSH) data,
OceanNet demonstrates competitive forecast skill by outperforming SSH
predictions by an uncoupled, state-of-the-art dynamical ocean model forecast,
reducing computation by 500,000 times. These accomplishments demonstrate the
potential of physics-inspired deep neural operators as cost-effective
alternatives to high-resolution numerical ocean models.