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