Interests
- M²LInES
- Momentum closures in ocean
- Large-eddy simulation
Latest
- Samudra 2: Scaling Ocean Emulators across Resolutions
- Calibration of a neural network ocean closure for improved mean state and variability
- Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
- Data-Driven Probabilistic Air-Sea Flux Parameterization
- A framework for hybrid physics-AI coupled ocean models
- Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
- Design and implementation of a data-driven parameterization for mesoscale thickness fluxes
- Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines
- Learning Machine Learning with Lorenz-96
- An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
- A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization
- Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields
- Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
- Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model
- Benchmarking of machine learning ocean subgrid parameterizations in an idealized model