Turbulence: Modeling & Simulations I: Data-Driven and Machine Learning Approaches

ORAL · A11 · ID: 22792

Presentations

  • ORAL

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    Publication: Y. Tian, Y. T. Lin, M. Anghel, and D. Livescu, "Data Driven Learning of Mori-Zwanzig Operators for Isotropic Turbulence" (planned).

    Presenters

    • Yifeng Tian

      • Los Alamos National Laboratory

    Authors

    • Yifeng Tian

      • Los Alamos National Laboratory

    • Yen Ting Lin

      • Los Alamos National Laboratory

    • Marian Anghel

      • Los Alamos National Laboratory

    • Daniel Livescu

      • Los Alamos Natl Lab
      • Los Alamos National Laboratory

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  • ORAL

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    Publication: Buzzicotti, M., Bonaccorso, F., Di Leoni, P. C., & Biferale, L. (2021). Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database. Physical Review Fluids, 6(5), 050503.

    Presenters

    • Michele Buzzicotti

      • Department of Physics and INFN University of Rome Tor Vergata.
      • Department of Physics & INFN, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy

    Authors

    • Michele Buzzicotti

      • Department of Physics and INFN University of Rome Tor Vergata.
      • Department of Physics & INFN, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy

    • Tianyi Li

      • Department of Mechanics and Aerospace Engineering, SUSTech, Shenzhen, China

    • Fabio Bonaccorso

      • Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, 00161 Roma, Italy

    • Patricio Clark Di Leoni

      • Johns Hopkins University
      • Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
      • Dept. of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA

    • Luca Biferale

      • University of Rome "Tor Vergata", Italy
      • University of Rome "Tor Vergata", INFN
      • University of Rome Tor Vergata
      • INFN - Rome
      • Department of Physics & INFN, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy

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  • ORAL

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    Publication: https://arxiv.org/abs/2103.07387

    Presenters

    • Ricardo Vinuesa

      • SimEx/FLOW, KTH Engineering Mechanics, Royal Institute of Technology, Stockholm, Sweden
      • KTH Royal Institute of Technology
      • KTH
      • SimEx/FLOW, KTH Engineering Mechanics

    Authors

    • Ricardo Vinuesa

      • SimEx/FLOW, KTH Engineering Mechanics, Royal Institute of Technology, Stockholm, Sweden
      • KTH Royal Institute of Technology
      • KTH
      • SimEx/FLOW, KTH Engineering Mechanics

    • Alejandro G\"uemes

      • Carlos III University

    • Hao Hu

      • KTH Royal Institute of Technolgoy

    • Stefano Discetti

      • Aerospace Engineering Research Group, Universidad Carlos III de Madrid, Leganes, Spain
      • Carlos III University

    • Andrea Ianiro

      • Aerospace Engineering Research Group, Universidad Carlos III de Madrid, Leganes, Spain
      • Carlos III University

    • Beril Sirmacek

      • Smart Cities, School of Creative Technology, Saxion University of Applied Sciences

    • Hossein Azizpour

      • Robotics, Perception and Learning (RPL), KTH Royal Institute of Technology
      • KTH Royal Institute of Technology

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  • ORAL

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    Publication: [1] Subel, Adam, Ashesh Chattopadhyay, Yifei Guan, and Pedram Hassanzadeh. "Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning." Physics of Fluids 33, no. 3 (2021): 031702.
    [2] Guan, Yifei, Ashesh Chattopadhyay, Adam Subel, and Pedram Hassanzadeh. "Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning." arXiv preprint arXiv:2102.11400 (2021).
    [3] Guan, Yifei, Adam Subel, Ashesh Chattopadhyay, and Pedram Hassanzadeh. "Developing data-driven subgrid-scale models for stable LES in the small-data limit: Applications of physics-constrained convolutional neural networks and data augmentation" (in preparation).

    Presenters

    • YIFEI GUAN

      • Rice University

    Authors

    • YIFEI GUAN

      • Rice University

    • Adam Subel

      • Rice Univ

    • Ashesh K Chattopadhyay

      • Rice University
      • Rice Univ

    • Pedram Hassanzadeh

      • Rice
      • Rice Univ

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  • ORAL

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    Publication: 1. Automating turbulence modelling by multi-agent reinforcement learning, Guido Novati, Hugues Lascombes de Laroussilhe & Petros Koumoutsakos, Nature Machine Intelligence, volume 3, pages 87–96 (2021)
    2. Scientific multi-agent reinforcement learning for wall-models of turbulent flows, Jane Bae and Petros Koumoutsakos, arXiv:2106.11144

    Presenters

    • Petros Koumoutsakos

      • Harvard University
      • ETH Zurich / Harvard University

    Authors

    • Petros Koumoutsakos

      • Harvard University
      • ETH Zurich / Harvard University

    • H. Jane Bae

      • California Institute of Technology
      • Caltech

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  • ORAL

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    Presenters

    • Michael Chertkov

      • University of Arizona

    Authors

    • Yifeng Tian

      • Los Alamos National Laboratory

    • Michael Chertkov

      • University of Arizona

    • Michael Woodward

      • University of Arizona

    • Mikhail Stepanov

      • University of Arizona

    • Chris Fryer

      • Los Alamos Natl Lab
      • Los Alamos National Laboratory

    • Criston M Hyett

      • University of Arizona
      • The University of Arizona

    • Daniel Livescu

      • Los Alamos Natl Lab
      • Los Alamos National Laboratory

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