Turbulence: Machine Learning Methods for Turbulence Modeling II
ORAL · Q22 · ID: 682576
Presentations
-
A deep learning based closure model for the multiscale evolution of Burgers turbulence
ORAL
–
Presenters
-
Mrigank Dhingra
Virginia Tech
Authors
-
Mrigank Dhingra
Virginia Tech
-
Anne E Staples
Virginia Tech
-
Omer San
Oklahoma State University-Stillwater, Oklahoma State University Stillwater, Oklahoma state
-
-
Data-enabled, progressive recalibration of the Spalart-Allmaras model for general purposes
ORAL
–
Presenters
-
Yuanwei Bin
Pennsylvania State University
Authors
-
Yuanwei Bin
Pennsylvania State University
-
Xiang F Yang
Pennsylvania State University
-
-
Reconstruction of Missing Flow Vectors Using Deep Image Inpainting Based on Partial Convolution Layer
ORAL
–
Presenters
-
Surabhi Singh
Embry-Riddle Aeronautical University, Daytona Beach
Authors
-
Surabhi Singh
Embry-Riddle Aeronautical University, Daytona Beach
-
Rahul Sengupta
University of Florida
-
Lawrence Ukeiley
University of Florida
-
-
A data-driven approach using CNN for wall modeling in Large Eddy Simulation
ORAL
–
Publication: Planned paper<br>Golsa Tabe Jamaat, Yuji Hattori, A non-local data-driven approach for wall modeling in LES (in preparation)
Presenters
-
Golsa Tabe Jamaat
Tohoku university
Authors
-
Golsa Tabe Jamaat
Tohoku university
-
Yuji Hattori
Institute of Fluid Science, Tohoku University, Tohoku Univ
-
-
Data-driven closure modeling for scale resolving PANS simulations in flows with coherent structures
ORAL
–
Presenters
-
Salar Taghizadeh
Texas A&M University
Authors
-
Salar Taghizadeh
Texas A&M University
-
Sharath S Girimaji
Texas A&M University
-
Freddie D Witherden
Texas A&M University
-
-
Regression-based projection for learning Mori-Zwanzig operators for isotropic turbulence
ORAL
–
Presenters
-
Yifeng Tian
Los Alamos National Laboratory
Authors
-
Yifeng Tian
Los Alamos National Laboratory
-
Yen Ting Lin
Los Alamos National Laboratory, LANL
-
Daniel Livescu
LANL, Los Alamos National Laboratory
-
-
Frame invariance and scalability of vector cloud neural network for partial differential equations
ORAL
–
Publication: Muhammad I. Zafar, Jiequn Han, Xu-Hui Zhou, and Heng Xiao, Frame invariance and scalability of neural operators for partial differential equations (Accepted for publishing in CiCP journal)<br><br>Jiequn Han, Xu-Hui Zhou, and Heng Xiao, VCNN-e: A vector-cloud neural network with equivariance for emulating Reynolds stress transport equations (to be submitted)
Presenters
-
Muhammad Irfan Zafar
Virginia Tech
Authors
-
Muhammad Irfan Zafar
Virginia Tech
-
Jiequn Han
Center for Computational Mathematics, Flatiron Institute, New York, USA
-
Xu-Hui Zhou
Virginia Tech
-
Heng Xiao
Virginia Tech
-
-
Self-Similar Stochastic Excitations For Linear Models In Turbulent Channel Flow
ORAL
–
Presenters
-
Jacob Holford
Imperial College London
Authors
-
Jacob Holford
Imperial College London
-
Myoungkyu Lee
The University of Alabama, University of Alabama
-
Yongyun Hwang
Imperial College London
-
-
Model-free forecasting of large partially observable spatiotemporally chaotic systems
ORAL
–
Presenters
-
Vikrant Gupta
Southern University of Science and Technology, Southern University of Science and Techn
Authors
-
Vikrant Gupta
Southern University of Science and Technology, Southern University of Science and Techn
-
Larry K.B. Li
The Hong Kong University of Science and Technology, Hong Kong University of Science and Technology
-
Shiyi Chen
Southern University of Science and Technology, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
-
Minping Wan
Southern University of Science and Technology, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
-