Modeling Methods IV: Data-driven and Machine-Learning Techniques

Presentations

• Shape-morphing modes for reduced-order modeling of advection-dominated flows with shallow neural networks

  ORAL

  Nov. 21, 2023, 12:50 PM – Nov. 21, 2023, 1:03 PM

  Presenters

  • Mohammad M Farazmand

    • North Carolina State University

  Authors

  • 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

  Nov. 21, 2023, 1:03 PM – Nov. 21, 2023, 1:16 PM

  Presenters

  • Zoey Flynn

    • University of Illinois Urbana-Champaign

  Authors

  • Zoey Flynn

    • University of Illinois Urbana-Champaign

  • Akhileshwar Borra

    • University of Illinois at Urbana-Champai

  • Andres Goza

    • University of Illinois at Urbana-Champaign

  • Theresa A Saxton-Fox

    • University of Illinois Urbana Champaign

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• Optimal linear model reduction using SPOD modes

  ORAL

  Nov. 21, 2023, 1:16 PM – Nov. 21, 2023, 1:29 PM

  Presenters

  • Peter K Frame

    • University of Michigan

  Authors

  • Peter K Frame

    • University of Michigan

  • Cong Lin

    • University of California, San Diego

  • Oliver T. Schmidt

    • University of California San Diego

  • Aaron S Towne

    • University of Michigan

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• Dynamics-preserving compression for modal flow analysis

  ORAL

  Nov. 21, 2023, 1:29 PM – Nov. 21, 2023, 1:42 PM

  Presenters

  • Anton Glazkov

    • KAUST

  Authors

  • Anton Glazkov

    • KAUST

  • Peter J Schmid

    • KAUST

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• Interpolatory input and output projections for flow control

  ORAL

  Nov. 21, 2023, 1:42 PM – Nov. 21, 2023, 1:55 PM

  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

  • Benjamin Herrmann

    • Universidad de Chile

  Authors

  • Benjamin Herrmann

    • Universidad de Chile

  • Scott T Dawson

    • Illinois Institute of Technology

  • Richard Semaan

    • Technische Universität Braunschweig

  • Steven L Brunton

    • University of Washington, Department of Mechanical Engineering
    • University of Washington

  • Beverley J McKeon

    • Stanford University

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• A parameterized LSTM deep neural network framework to model unsteady flow problems

  ORAL

  Nov. 21, 2023, 1:55 PM – Nov. 21, 2023, 2:08 PM

  Presenters

  • Hamid Reza Karbasian

    • Massachusetts Institute of Technology

  Authors

  • Hamid Reza Karbasian

    • Massachusetts Institute of Technology

  • 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

  Nov. 21, 2023, 2:08 PM – Nov. 21, 2023, 2:21 PM

  Presenters

  • Georgios Rigas

    • Imperial College London

  Authors

  • Georgios Rigas

    • Imperial College London

  • 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

  Nov. 21, 2023, 2:21 PM – Nov. 21, 2023, 2:34 PM

  Publication: Model Order Reduction of Scramjet Isolator Shock Dynamics During Unstart, ASME Conference Paper 2022
  Extracting Low Rank Dynamics from Statistically Non-Stationary Fluid Flows Using a Shift Procedure, ASME Journal Paper, Planned

  Presenters

  • Jack Sullivan

    • Ohio State University

  Authors

  • Jack Sullivan

    • Ohio State University

  • 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

  Nov. 21, 2023, 2:34 PM – Nov. 21, 2023, 2:47 PM

  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

  • Konduri Aditya

    • Indian Institute of Science Bangalore

  Authors

  • Konduri Aditya

    • Indian Institute of Science Bangalore

  • Dibyajyoti Nayak

    • Indian Institute of Science Bangalore

  • Anirudh Jonnalagadda

    • Indian Institute of Science Bangalore

  • Uma Balakrishnan

    • Sandia National Laboratories, Livermore
    • Sandia National Laboratories

  • 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

  Nov. 21, 2023, 2:47 PM – Nov. 21, 2023, 3:00 PM

  Presenters

  • Dongjie Jia

    • Cornell University

  Authors

  • Dongjie Jia

    • Cornell University

  • Mahdi Esmaily

    • Cornell University

  View abstract →