Data-Driven Approaches to Fluid Dynamics
ORAL · A29 · ID: 1765537
Presentations
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Unraveling Turbulent Pipe Flow Dynamics: Data-Driven Models at Re = 2500
ORAL
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Presenters
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C. Ricardo Constante-Amores
University of Wisconsin-Madison
Authors
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C. Ricardo Constante-Amores
University of Wisconsin-Madison
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Alec J Linot
University of California- Los Angeles
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Michael D Graham
University of Wisconsin - Madison
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Data-driven resolvent analysis of turbulent channel flow
ORAL
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Presenters
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Carlos G Gonzalez Hernandez
Stanford University
Authors
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Carlos G Gonzalez Hernandez
Stanford University
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Benjamin Herrmann
Universidad de Chile
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Katherine Cao
Stanford University
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Matthew J Colbrook
DAMTP, Cambridge University
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Steven L Brunton
University of Washington, Department of Mechanical Engineering, University of Washington
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Beverley J McKeon
Stanford University
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Low-dimensional data-driven modeling through temporally parameterized neural networks for geophysical forecasting
ORAL
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Presenters
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Andrew J Fox
University of Wisconsin-Madison
Authors
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Andrew J Fox
University of Wisconsin-Madison
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Michael D Graham
University of Wisconsin - Madison
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Assessment of Sonic Boom Loudness of Supersonic Aircraft using Reduced Order Modeling
ORAL
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Publication: Journal paper to be submitted in September 2023
Presenters
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Nikhil Iyengar
Georgia Institute of Technology
Authors
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Nikhil Iyengar
Georgia Institute of Technology
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Dimitri Mavris
Georgia Institute of Technology
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Abstract Withdrawn
ORAL Withdrawn
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Data-driven compression of plunging airfoil wakes
ORAL
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Presenters
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Hiroto Odaka
UCLA
Authors
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Hiroto Odaka
UCLA
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Kai Fukami
UCLA
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Kunihiko Taira
UCLA, University of California, Los Angeles
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Station-data-driven temperature forecasting in South Korea using convolutional neural networks
ORAL
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Presenters
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Jun Park
Yonsei University
Authors
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Jun Park
Yonsei University
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A POD-driven Machine Learning Algorithm to predict 3D Patient-Specific Aortic flows
ORAL
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Presenters
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Chotirawee Chatpattanasiri
University College London
Authors
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Chotirawee Chatpattanasiri
University College London
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Catriona Stokes
University College London
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Vanessa Diaz-Zuccarini
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, UK;Department of Mechanical Engineering, University College London, UK, University College London
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Stavroula Balabani
1. Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, UK; 2. Department of Mechanical Engineering, University College London, UK, University College London
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Data-driven constitutive relation reveals scaling law for hydrodynamic transport coefficients
ORAL
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Publication: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.015104
Presenters
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Candi Zheng
Hong Kong University of Science and Technology & Southern University of Science and Technology
Authors
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Candi Zheng
Hong Kong University of Science and Technology & Southern University of Science and Technology
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Yang Wang
Hong Kong University of Science and Technology
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Shiyi Chen
Southern University Of Science and Technology
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