Work

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

Year: 2020

Type: article

Source: Theoretical and Computational Fluid Dynamics

DOI: 10.1007/s00162-019-00512-z

Authors: Suraj Pawar, Omer San, Adil Rasheed, Prakash Vedula

Institutions: Oklahoma State University, Norwegian University of Science and Technology, University of Oklahoma

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research

Awards: PHYSICS-REINFORCED MACHINE LEARNING ALGORITHMS FOR MULTISCALE CLOSURE MODEL DISCOVERY, G6951186196, PHYSICS-REINFORCED MACHINE LEARNING ALGORITHMS FOR MULTISCALE CLOSURE MODEL DISCOVERY

OpenAlex ID: W2980346118

FWCI: 3.917

Cites: 102

Cited by: 62

Related to: 10