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, 3:50 PM – Nov. 25, 2024, 4:03 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, 4:03 PM – Nov. 25, 2024, 4:16 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

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

  ORAL

  Nov. 25, 2024, 4:16 PM – Nov. 25, 2024, 4:29 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, 4:29 PM – Nov. 25, 2024, 4:42 PM

  Presenters

  • Parvin Bayati

    Pennsylvania State University

  Authors

  • Parvin Bayati

    Pennsylvania State University

  • Stewart Mallory

    Pennsylvania State University

  View abstract →

• Machine learning models for unresolved capillary effects in multiphase flows

  ORAL

  Nov. 25, 2024, 4:42 PM – Nov. 25, 2024, 4:55 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

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

  ORAL

  Nov. 25, 2024, 4:55 PM – Nov. 25, 2024, 5:08 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, 5:08 PM – Nov. 25, 2024, 5:21 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

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

  ORAL

  Nov. 25, 2024, 5:21 PM – Nov. 25, 2024, 5:34 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, 5:34 PM – Nov. 25, 2024, 5:47 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 →