64 citations · 64 across the 1 of their papers we have counts for
12 papers
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…
An Eddy-Zonal Flow Feedback Model for Propagating Annular Modes
Sandro W. Lubis, Pedram Hassanzadeh
The variability of the zonal-mean large-scale extratropical circulation is often studied using individual modes obtained from empirical orthogonal function (EOF) analyses. The prev…
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh
To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-…
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…
Analog forecasting of extreme-causing weather patterns using deep learning
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Pedram Hassanzadeh
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-dri…
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lore…