Low-Order Modeling and Machine Learning in Fluid Dynamics: Other Applications I

Presentations

• Realtime data-driven sensing of oscillatory crossflow using a fixed-wing drone

  ORAL

  Nov. 25, 2024, 1:50 PM – Nov. 25, 2024, 2:03 PM

  Presenters

  • Xiaozhou Fan

    • Caltech

  Authors

  • Xiaozhou Fan

    • Caltech

  • Fengze Xie

    • Caltech

  • Julian Humml

    • Caltech

  • Jacob Schuster

    • Caltech

  • Yisong Yue

    • Caltech

  • Morteza Gharib

    • Caltech

  View abstract →

• Data-driven prediction of unsteady loading on a 2D deforming airfoil

  ORAL

  Nov. 25, 2024, 2:03 PM – Nov. 25, 2024, 2:16 PM

  Presenters

  • Hamid Reza Karbasian

    • Southern Methodist University

  Authors

  • Hamid Reza Karbasian

    • Southern Methodist University

  • Wim M. van Rees

    • Massachusetts Institute of Technology MI
    • Massachusetts Institute of Technology MIT

  View abstract →

• A Physics-Infused, Machine Learning Framework to Study Wind-Driven Runback Water Flows Pertinent to Aircraft Icing Phenomena

  ORAL

  Nov. 25, 2024, 2:16 PM – Nov. 25, 2024, 2:29 PM

  Presenters

  • Jincheng Wang

    • Iowa State University

  Authors

  • Jincheng Wang

    • Iowa State University

  • Charlelie Laurent

    • Stanford University

  • Suhas S Jain

    • Center for Turbulence Research

  • Hui Hu

    • Iowa State University

  View abstract →

• Utilizing Physics-Informed Neural Networks (PINNs) to Estimate Non-Uniform Surface Properties of Active Droplets

  ORAL

  Nov. 25, 2024, 2:29 PM – Nov. 25, 2024, 2:42 PM

  Presenters

  • Parvin Bayati

    • Pennsylvania State University

  Authors

  • Parvin Bayati

    • Pennsylvania State University

  • Stewart Mallory

    • Pennsylvania State University

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• Machine learning models for unresolved capillary effects in multiphase flows

  ORAL

  Nov. 25, 2024, 2:42 PM – Nov. 25, 2024, 2:55 PM

  Presenters

  • Shahab Mirjalili

    • Stanford University
    • Department of Mechanical Engineering, Stanford University

  Authors

  • Shahab Mirjalili

    • Stanford University
    • Department of Mechanical Engineering, Stanford University

  • Chris James Cundy

    • Stanford University

  • Charlelie Laurent

    • Stanford University

  • Stefano Ermon

    • Stanford University

  • Gianluca Iaccarino

    • Stanford University

  • Ali Mani

    • Stanford University

  View abstract →

• Convolutional feature-enhanced physics-informed neural networks for the spatio-temporal reconstruction of two-phase flows

  ORAL

  Nov. 25, 2024, 2:55 PM – Nov. 25, 2024, 3:08 PM

  Presenters

  • Maximilian Dreisbach

    • Institute of Fluid Mechanics, Karlsruhe Institute of Technology, Kaiserstraße 10, 76131 Karlsruhe, Germany
    • Karlsruhe Institute of Technology

  Authors

  • Maximilian Dreisbach

    • Institute of Fluid Mechanics, Karlsruhe Institute of Technology, Kaiserstraße 10, 76131 Karlsruhe, Germany
    • Karlsruhe Institute of Technology

  • Elham Kiyani

    • Division of Applied Mathematics and School of Engineering, Brown University, Providence, RI, 02912, USA
    • Division of Applied Mathematics, Brown University, Providence, RI, 02912, USA

  • Jochen Kriegseis

    • Karlsruhe Institute of Technology

  • George Em Karniadakis

    • Division of Applied Mathematics and School of Engineering, Brown University, Providence, RI, 02912, USA
    • Brown University

  • Alexander Stroh

    • Institute of Fluid Mechanics, Karlsruhe Institute of Technology, Kaiserstraße 10, 76131 Karlsruhe, Germany
    • Karlsruhe Institute of Technology

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• Towards spatio-temporal prediction of cavitating fluid flow with graph neural networks

  ORAL

  Nov. 25, 2024, 3:08 PM – Nov. 25, 2024, 3:21 PM

  Publication: Gao, Rui, Shayan Heydari, and Rajeev K. Jaiman. "Towards spatio-temporal prediction of cavitating fluid flow with graph neural networks." International Journal of Multiphase Flow 177 (2024): 104858.

  Presenters

  • Rui Gao

    • University of British Columbia

  Authors

  • Rui Gao

    • University of British Columbia

  • Shayan Heydari

    • University of British Columbia

  • Rajeev Jaiman

    • University of British Columbia

  View abstract →

• Efficient Estimation of Temporal Exceeding Probability for Ship Responses in Broad-Band Wave Fields

  ORAL

  Nov. 25, 2024, 3:21 PM – Nov. 25, 2024, 3:34 PM

  Presenters

  • Shayesteh Hafezi

    • University of Michigan

  Authors

  • Shayesteh Hafezi

    • University of Michigan

  • Xianliang Gong

    • University of Michigan

  • Yulin Pan

    • University of Michigan

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• Nonlinear energy amplification of turbulent flows over progressive surface waves

  ORAL

  Nov. 25, 2024, 3:34 PM – Nov. 25, 2024, 3:47 PM

  Presenters

  • Ziyan Ren

    • University of Minnesota

  Authors

  • Ziyan Ren

    • University of Minnesota

  • Anqing Xuan

    • University of Minnesota

  • Lian Shen

    • University of Minnesota

  View abstract →