Title: Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Neural Operators, Downscaling Models, Supervised Deep Learning, Oceanography

Abstract: Accurate modeling of physical systems governed by partial differential equations
is a central challenge in scientific computing. In oceanography, high-resolution
current data are critical for coastal management, environmental monitoring, and
maritime safety. However, available satellite products, such as Copernicus data
for sea water velocity at ∼0.08° spatial resolution and global ocean models, often
lack the spatial granularity required for detailed local analyses. In this work, we
(a) introduce a supervised deep learning framework based on neural operators
for solving PDEs and providing arbitrary resolution solutions, and (b) propose
downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary
resolution, regardless of the input resolution. We evaluated our model on real-world
Copernicus ocean current data and synthetic Navier–Stokes simulation datasets.