455 citations · 2.1k across the 56 of their papers we have counts for
16 papers · 1 filter
Semi-analytical eddy-viscosity and backscattering closures for 2D geophysical turbulence
Yifei Guan, Pedram Hassanzadeh
Physics-based eddy-viscosity and backscattering closures are widely used for large-eddy simulation (LES) of geophysical turbulence, but their key parameters are often chosen empiri…
Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion
Yifei Guan, Pedram Hassanzadeh, Tapio Schneider +4
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and…
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
Karan Jakhar, Yifei Guan, Rambod Mojgani +2
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we app…
Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
Ashesh Chattopadhyay, Y. Qiang Sun, Pedram Hassanzadeh
Long-term stability and physical consistency are critical properties for AI-based weather models if they are going to be used for subseasonal-to-seasonal forecasts or beyond, e.g.,…
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
Adam Subel, Yifei Guan, Ashesh Chattopadhyay +1
Transfer learning (TL) is becoming a powerful tool in scientific applications of neural networks (NNs), such as weather/climate prediction and turbulence modeling. TL enables out-o…
Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
Yifei Guan, Adam Subel, Ashesh Chattopadhyay +1
We demonstrate how incorporating physics constraints into convolutional neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy simu…
Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning
Yifei Guan, Ashesh Chattopadhyay, Adam Subel +1
There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some rece…
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
Adam Subel, Ashesh Chattopadhyay, Yifei Guan +1
Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LES) has received substantial attention recently. Despite some success, particularly in a priori (offl…
A Koopman-based framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings
Mohammad Amin Khodkar, Pedram Hassanzadeh, Athanasios Antoulas
We introduce a data-driven method and shows its skills for spatiotemporal prediction of high-dimensional chaotic dynamics and turbulence. The method is based on a finite-dimensiona…
Data-driven Spatio-temporal Prediction of High-dimensional Geophysical Turbulence using Koopman Operator Approximation
M. A. Khodkar, Athanasios C. Antoulas, Pedram Hassanzadeh
We show the skills of a data-driven low-dimensional linear model in predicting the spatio-temporal evolution of turbulent Rayleigh-Bénard convection. The model is based on dynamic…
Data-driven reduced modelling of turbulent Rayleigh-Benard convection using DMD-enhanced Fluctuation-Dissipation Theorem
M. A. Khodkar, Pedram Hassanzadeh
A data-driven, model-free framework is introduced for calculating Reduced-Order Models (ROMs) capable of accurately predicting time-mean responses to external forcings, or forcings…
Reduced-order modeling of fully turbulent buoyancy-driven flows using the Green's function method
M. A. Khodkar, Pedram Hassanzadeh, Saleh Nabi +1
A One-Dimensional (1D) Reduced-Order Model (ROM) has been developed for a 3D Rayleigh-Bénard convection system in the turbulent regime with Rayleigh number . The…
Quantifying the eddy-jet feedback strength of the annular mode in an idealized GCM and reanalysis data
Ding Ma, Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) that relates the temporal tendency of zonal mean temperature and zonal wind to their anomalies and external forcing is used to accurately quantify…
The linear response function of an idealized atmosphere. Part 2: Implications for the practical use of the Fluctuation-Dissipation Theorem and the role of operator's non-normality
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) relates the mean-response of a nonlinear system to weak external forcings and vice versa. Even for simple models of the general circulation, such a…
The linear response function of an idealized atmosphere. Part 1: Construction using Green's functions and applications
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) determines the mean-response of a nonlinear climate system to weak imposed forcings, and an eddy flux matrix (EFM) determines the eddy momentum and…
The Universal Aspect Ratio of Vortices in Rotating Stratified Flows: Theory and Simulation
Pedram Hassanzadeh, Philip S. Marcus, Patrice Le Gal
We derive a relationship for the vortex aspect ratio (vertical half-thickness over horizontal length scale) for steady and slowly evolving vortices in rotating stratified fluid…