Low-Order Modeling and Machine Learning for Turbulence I
ORAL · J29 · ID: 1765165
Presentations
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A priori screening of machine-learning turbulence models
ORAL
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Presenters
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Peng Chen
College of Engineering, SUSTech
Authors
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Peng Chen
College of Engineering, SUSTech
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Yuanwei Bin
Pennsylvania State University & Peking University, Pennsylvania State University
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Yipeng Shi
Peking University
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Mahdi Abkar
Aarhus University
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George I Park
University of Pennsylvania
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Xiang Yang
Pennsylvania State University, The Penn State Department of Mechanical Engineering, Penn State Department of Mechanical Engineering
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Data-driven classification of sheared stratified turbulence from experimental shadowgraphs
ORAL
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Publication: https://arxiv.org/abs/2305.04051<br>https://arxiv.org/abs/2305.04048
Presenters
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Miles M Couchman
Department of Mathematics and Statistics, York University
Authors
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Miles M Couchman
Department of Mathematics and Statistics, York University
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Adrien Lefauve
DAMTP, University of Cambridge
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Enhancing Wall-Bounded Turbulence Simulation through Differentiable Neural Wall Modeling
ORAL
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Presenters
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Xiantao Fan
University of Notre Dame
Authors
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Xiantao Fan
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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A posteriori learning of closures for geophysical turbulence using ensemble Kalman inversion
ORAL
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Presenters
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Yifei Guan
Rice University
Authors
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Yifei Guan
Rice University
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Pedram Hassanzadeh
Rice University
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Tapio Schneider
California Institute of Technology, Pasadena, CA 91125
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Zhengyu Huang
California Institute of Technology, Pasadena, CA 91125
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Oliver Dunbar
California Institute of Technology
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Ignacio Lopez-Gomez
Google research
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Jinlong Wu
University of Wisconsin-Madison, University of Wisconsin - Madison, University of Wisconsin–Madison
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Velocity gradient prediction using parameterized Lagrangian deformation models
ORAL
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Presenters
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Criston M Hyett
The University of Arizona
Authors
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Criston M Hyett
The University of Arizona
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Yifeng Tian
Los Alamos National Laboratory
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Mikhail Stepanov
The University of Arizona
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Daniel Livescu
LANL
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Michael Chertkov
University of Arizona
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Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
ORAL
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Publication: Jakhar, K., Guan, Y., Mojgani, R., Chattopadhyay, A., Hassanzadeh, P., & Zanna, L. (2023). Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges. arXiv preprint arXiv:2306.05014.
Presenters
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Karan Jakhar
Rice University
Authors
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Karan Jakhar
Rice University
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Yifei Guan
Rice University
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Rambod Mojgani
Rice University
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Ashesh K Chattopadhyay
University of California, Santa Cruz
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Pedram Hassanzadeh
Rice University
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Laura Zanna
Courant Institute of Mathematical Sciences
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Removing the log-layer mismatch in wall-modeled LES using near-wall erroneous flows via physics-informed neural network
ORAL
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Presenters
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Soju Maejima
Tohoku University
Authors
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Soju Maejima
Tohoku University
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Soshi Kawai
Tohoku Univ, Tohoku University
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Resolvent analysis of turbulent flows over progressive surface waves
ORAL
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Presenters
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Ziyan Ren
University of Minnesota
Authors
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Ziyan Ren
University of Minnesota
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Anqing Xuan
University of Minnesota
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Lian Shen
University of Minnesota
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Convective parametrization of dry atmospheric boundary layer by generative machine learning model
ORAL
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Presenters
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Joerg Schumacher
Technische Universität Ilmenau, TU Ilmenau
Authors
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Joerg Schumacher
Technische Universität Ilmenau, TU Ilmenau
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Florian Heyder
Tech Univ Ilmenau
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Juan Pedro Mellado
University of Hamburg
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