Climate & Ocean Physics @ NYU
Climate & Ocean Physics @ NYU
Tour
People
News
Projects
Publications
Datasets
Talks
Jobs
Contact
Light
Dark
Automatic
Pavel Perezhogin
Postdoctoral researcher
New York University
Interests
M²LInES
Momentum closures in ocean
Large-eddy simulation
Latest
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
Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
Learning Machine Learning with Lorenz-96
An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields
Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
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
Causal inference in spatiotemporal climate fields through linear response theory
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
Cite
×