Modeling Methods IV: Data-driven and Machine-Learning Techniques
ORAL · ZC29 · ID: 1765286
Presentations
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Shape-morphing modes for reduced-order modeling of advection-dominated flows with shallow neural networks
ORAL
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Presenters
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Mohammad M Farazmand
North Carolina State University
Authors
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Mohammad M Farazmand
North Carolina State University
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A phase-based proper orthogonal decomposition that accounts for intrinsic large scale motion
ORAL
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Presenters
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Zoey Flynn
University of Illinois Urbana-Champaign
Authors
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Zoey Flynn
University of Illinois Urbana-Champaign
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Akhileshwar Borra
University of Illinois at Urbana-Champai
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Andres Goza
University of Illinois at Urbana-Champaign
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Theresa A Saxton-Fox
University of Illinois Urbana Champaign
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Optimal linear model reduction using SPOD modes
ORAL
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Presenters
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Peter K Frame
University of Michigan
Authors
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Peter K Frame
University of Michigan
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Cong Lin
University of California, San Diego
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Oliver T. Schmidt
University of California San Diego
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Aaron S Towne
University of Michigan
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Dynamics-preserving compression for modal flow analysis
ORAL
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Presenters
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Anton Glazkov
KAUST
Authors
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Anton Glazkov
KAUST
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Peter J Schmid
KAUST
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Interpolatory input and output projections for flow control
ORAL
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Publication: Herrmann, B., Baddoo, P. J., Dawson, S., Semaan, R., Brunton, S. L., & McKeon, B. J. (2023). From resolvent to Gramians: extracting forcing and response modes for control. arXiv preprint arXiv:2301.13093.
Presenters
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Benjamin Herrmann
Universidad de Chile
Authors
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Benjamin Herrmann
Universidad de Chile
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Scott T Dawson
Illinois Institute of Technology
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Richard Semaan
Technische Universität Braunschweig
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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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A parameterized LSTM deep neural network framework to model unsteady flow problems
ORAL
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Presenters
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Hamid Reza Karbasian
Massachusetts Institute of Technology
Authors
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Hamid Reza Karbasian
Massachusetts Institute of Technology
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Wim M. M van Rees
Massachusetts Institute of Technology MI, Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology
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Data-driven closure of the harmonic-balanced Navier-Stokes equations in the frequency domain
ORAL
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Presenters
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Georgios Rigas
Imperial College London
Authors
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Georgios Rigas
Imperial College London
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Peter J Schmid
King Abdullah University of Science and Technology
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A Shift Procedure for Identifying Low Rank Behavior from Non-Stationary Dynamical System Data
ORAL
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Publication: Model Order Reduction of Scramjet Isolator Shock Dynamics During Unstart, ASME Conference Paper 2022<br>Extracting Low Rank Dynamics from Statistically Non-Stationary Fluid Flows Using a Shift Procedure, ASME Journal Paper, Planned
Presenters
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Jack Sullivan
Ohio State University
Authors
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Jack Sullivan
Ohio State University
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Datta V Gaitonde
Ohio State University, Ohio State Univ - Columbus
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A co-kurtosis PCA based dimensionality reduction with neural network reconstruction for chemical kinetics in reacting flows
ORAL
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Publication: Nayak, D., Jonnalagadda, A., Balakrishnan, U., Kolla, H., & Aditya, K. (2023). A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks. arXiv preprint arXiv:2307.03289.
Presenters
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Konduri Aditya
Indian Institute of Science Bangalore
Authors
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Konduri Aditya
Indian Institute of Science Bangalore
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Dibyajyoti Nayak
Indian Institute of Science Bangalore
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Anirudh Jonnalagadda
Indian Institute of Science Bangalore
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Uma Balakrishnan
Sandia National Laboratories, Livermore, Sandia National Laboratories
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Hemanth Kolla
Sandia National Laboratories, Livermore, Sandia National Laboratories
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Toward Real-Time Simulation of Cardiovascular Flows by Introducing a Stabilized Frequency Finite Element Methods
ORAL
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Presenters
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Dongjie Jia
Cornell University
Authors
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Dongjie Jia
Cornell University
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Mahdi Esmaily
Cornell University
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