Data-Driven Modeling, Control and Analysis for Fluid Dynamics
ORAL · G29 · ID: 1765590
Presentations
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The neural network fluid dynamicist: networks in feedback for flow control, sensor placement, and flow physics analysis
ORAL
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Publication: T. Deda, W. R. Wolf, and S. T. M. Dawson, "Backpropagation of neural network dynamical models applied to flow control," Theoretical and Computational Fluid Dynamics, 37, pp. 35–59, 2023.
Presenters
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Scott T Dawson
Illinois Institute of Technology
Authors
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Scott T Dawson
Illinois Institute of Technology
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Tarcísio C Oliveira
UNICAMP-Univ de Campinas
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William R Wolf
University of Campinas
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Leveraging Bayesian Optimisation for Expensive Experiments and Simulations in Fluid Dynamics with Uncontrollable Dynamic Variables
ORAL
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Presenters
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Mike Diessner
Newcastle University
Authors
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Mike Diessner
Newcastle University
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Joseph O'Connor
Imperial College London
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Andrew Wynn
Imperial College London
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Sylvain Laizet
Imperial College London
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Xiaonan Chen
School of Engineering, Newcastle University, Newcastle University
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Kevin Wilson
Newcastle University
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Richard D Whalley
Newcastle Uiveristy, Newcastle University
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Phase-oscillator-based modeling and control of multi-modal fluid flows
ORAL
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Presenters
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Vedasri Godavarthi
University of California, Los Angeles
Authors
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Vedasri Godavarthi
University of California, Los Angeles
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Yoji Kawamura
Japan Agency for Marine-Earth Science and Technology
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Kunihiko Taira
UCLA, University of California, Los Angeles
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Discovering sparse optimal finite-amplitude perturbations in nonlinear flows
ORAL
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Presenters
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A. Leonid Heide
University of Minnesota
Authors
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A. Leonid Heide
University of Minnesota
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Maziar S Hemati
University of Minnesota
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Data-Driven Sensor Placement for Nuclear Reactor Transient Analyses in Digital Twins
ORAL
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Presenters
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Niharika Karnik
University of Washington
Authors
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Niharika Karnik
University of Washington
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Krithika Manohar
University of Washington
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Mohammad G Abdo
Idaho National Laboratory
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