Lupin-Jimenez, L. (AM) – Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation, Spectral Bias in Diffusion Models, and Equation Discovery
Hybrid Event
Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers. Their scientific utility depends on physical consistency, which for the systems studied here rests in large part on spectral fidelity, the accurate reconstruction of variance across spatial scales. This document presents two published studies and two studies in progress that develop, analyze, and apply data-driven methods for turbulent phenomena along that thread. The first study develops FCDS, a framework that autoregressively emulates surface ocean dynamics over the Gulf of Mexico at 8 km resolution and […]
