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Machine Learning for Inference and Analysis of Fluid Flows

ORAL · L17 · ID: 1765485

Presentations

  • Overview of a database for reduced-complexity modeling of fluid flows

    ORAL

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    Publication: Towne, A., Dawson, S.T.M., Brès, G.A, Lozano-Durán, A., Saxton-Fox, T., Parthasarathy, A., Jones, A.R., Biler, H., Yeh, C.-A. Patel, H.D., Taira, K. (2023). A Database for Reduced-Complexity Modeling of Fluid Flows. AIAA Journal, 61 (7), 2867-2892.

    Presenters

    • Aaron S Towne

      University of Michigan

    Authors

    • Aaron S Towne

      University of Michigan

    • Scott T Dawson

      Illinois Institute of Technology

    • Guillaume Bres

      Cascade Technologies

    • Adrian Lozano-Duran

      MIT, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology

    • Theresa A Saxton-Fox

      University of Illinois Urbana Champaign

    • Aadhy S Parthasarathy

      University of Illinois at Urbana-Champai

    • Anya R Jones

      U Maryland

    • Hulya Biler

      University of Maryland, University of Southampton, University of southampton

    • Chi-An Yeh

      North Carolina State University

    • Het D Patel

      North Carolina State University

    • Kunihiko Taira

      UCLA, University of California, Los Angeles

    View abstract →

  • Reduced Order Modelling for Urban UAS Wind Field Estimation: A Neural Galerkin Projection Approach

    ORAL

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    Presenters

    • Rohit Kameshwara Sampath Sai K Vuppala

      Oklahoma State University-Stillwater

    Authors

    • Rohit Kameshwara Sampath Sai K Vuppala

      Oklahoma State University-Stillwater

    • Shane Coffing

      Los Alamos National Lab

    • Arvind T Mohan

      Los Alamos National Laboratory

    • Kursat Kara

      Oklahoma State University-Stillwater, Oklahoma State University

    View abstract →

  • Extreme Aerodynamic Manifold: Vortex-Airfoil Interactions

    ORAL

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    Publication: Fukami, K., & Taira, K. (2023). Grasping extreme aerodynamics on a low-dimensional manifold. arXiv preprint arXiv:2305.08024.

    Presenters

    • Kai Fukami

      UCLA

    Authors

    • Kai Fukami

      UCLA

    • Kunihiko Taira

      UCLA, University of California, Los Angeles

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  • Analyzing the relationship between wake flow patterns and design element changes of automobile using machine learning

    ORAL

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    Publication: 1. S. L. Brunton, "Applying machine learning to study of fluid mechanics." Acta Mech. Sin., 37, 1718-1726, 2021.<br>2. R. Machuca and K. Phillips, "Applications of Vector Fields to Image Processing," IEEE Transactions on Pattern Analysis and Machine Intelligence, 316-329, 1983.<br>3. Z. Xu, et al., "A diagram of evaluating multiple aspects of model performance in simulating vector fields," Geosci. Model Dev. 9, 4365-4380, 2016.<br>4. K.He, et al., "Deep Residual Learning for Image Recognition," Computer Vision Foundation, 2015.

    Presenters

    • Jun Kim

      Department of Mechanical Engineering, Hanyang University

    Authors

    • Jun Kim

      Department of Mechanical Engineering, Hanyang University

    • Ilhoon Jang

      Hanyang University, Department of Mechanical Engineering, Hanyang University

    • Je Hyeong Hong

      Department of Electronic Engineering, Hanyang University

    • Chanhyuk Yun

      Department of Electronic Engineering, Hanyang University

    • Simon Song

      Hanyang University, Department of Mechanical Engineering, Hanyang University

    View abstract →

  • In Situ Anomaly Detection in Turbulent Reacting Flows at the Exascale

    ORAL

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    Presenters

    • Jorge Salinas

      Sandia National Laboratories, University of Florida (past) and Combustion Research Facility, Sandia National Laboratories (current)

    Authors

    • Jorge Salinas

      Sandia National Laboratories, University of Florida (past) and Combustion Research Facility, Sandia National Laboratories (current)

    • Hemanth Kolla

      Sandia National Laboratories, Livermore, Sandia National Laboratories

    • Martin Rieth

      Sandia National Laboratories

    • Jacqueline H Chen

      Sandia National Laboratories, Sandia National Labs

    • Janine C Bennett

      Sandia National Laboratories

    • Marco Arienti

      Sandia National Laboratories

    • Nicole Marsaglia

      Lawrence Livermore National Laboratory

    • Cyrus Harrison

      Lawrence Livermore National Laboratory

    View abstract →

  • Analyzing the Dynamics of Discrete Gust Encounters with Persistent Homology

    ORAL

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    Publication: L. Smith, K. Fukami, G. Sedky, A. Jones, and K. Taira, "A cyclic perspective on transient gust encounters through the lens of persistent homology," Journal of Fluid Mechanics, in review, 2023.

    Presenters

    • Luke Smith

      UCLA, University of California, Los Angeles

    Authors

    • Luke Smith

      UCLA, University of California, Los Angeles

    • Kai Fukami

      UCLA

    • Girguis Sedky

      Princeton University

    • Anya R Jones

      U Maryland

    • Kunihiko Taira

      UCLA, University of California, Los Angeles

    View abstract →

  • ChatGPT for Programming Numerical Problems of Fluid Mechanics

    ORAL

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    Publication: "ChatGPT for programming numerical methods"<br>Link to the journal paper:<br>https://www.dl.begellhouse.com/journals/558048804a15188a,498820861ef102d2,1255e053242c9a40.html

    Presenters

    • Ali Kashefi

      Stanford University

    Authors

    • Ali Kashefi

      Stanford University

    • Tapan Mukerji

      Stanford University

    View abstract →