Machine Learning for Inference and Analysis of Fluid Flows

ORAL · L17 · ID: 1765485

Presentations

  • ORAL

    –

    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 →

  • ORAL

    –

    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 →

  • ORAL

    –

    Publication: 1. S. L. Brunton, "Applying machine learning to study of fluid mechanics." Acta Mech. Sin., 37, 1718-1726, 2021.
    2. R. Machuca and K. Phillips, "Applications of Vector Fields to Image Processing," IEEE Transactions on Pattern Analysis and Machine Intelligence, 316-329, 1983.
    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.
    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 →

  • ORAL

    –

    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 →

  • ORAL

    –

    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 →

  • ORAL

    –

    Publication: "ChatGPT for programming numerical methods"
    Link to the journal paper:
    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 →