OceanNet: A principled neural operator-based digital twin for regional oceans
This addresses the problem of high computational costs in ocean modeling for researchers and forecasters, offering a cost-effective alternative, though it is incremental as it builds on existing neural operator methods.
The study tackled ocean modeling challenges by introducing OceanNet, a neural operator-based digital twin for ocean circulation, which demonstrated competitive forecast skill by outperforming a state-of-the-art dynamical model in sea surface height predictions and reducing computation by 500,000 times.
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.