114 citations · 337 across the 5 of their papers we have counts for
5 papers
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…
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-…