Learning equations for ocean turbulence using machine learning

Neural Nets + Physics vs. Equation-discovery for parameterization

Our new work with Tom Bolton on physics-aware & interpretable ML to improve ocean models is out in GRL.

Our new approach to the parameterization/closure problem: learning differential equations of missing physics in coarse-res ocean models from data. We use a machine learning (ML) method that relies on sparse Bayesian inference / relevance vector machine to learn ocean eddy parameterizations of momentum, buoyancy, and energy. We compare it to a ML parameterization which uses convolutional neural nets with conservation-law embedded in the architecture. Each approach has pros and cons (interpretability, generalization, numerical stability) in our proof-of-concept. Many aspects need to be improved but we are moving one step closer towards blending physics and machine learning for climate modeling.

Laure Zanna
Laure Zanna
Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics; Professor of Mathematics and Data Science

My research interests include Climate Dynamics, Physical Oceanography and Data Science.