CFD: Data-Driven and Machine Learning

Presentations

• Machine learning framework to predict flows over arbitrarily arranged solid arrays

  ORAL

  Nov. 21, 2022, 4:10 PM – Nov. 21, 2022, 4:23 PM

  Presenters

  • Geunhyeok Choi

    • Hongik University

  Authors

  • Geunhyeok Choi

    • Hongik University

  • Seungwon Shin

    • Department of Mechanical and System Design Engineering, Hongik University

  • Seong Jin Kim

    • Extreme Materials Research Center, Korea Institute of Science and Technology
    • KIST

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• A Neural Differentiable Solver for Efficient Simulation of Fluid-Structure Interaction

  ORAL

  Nov. 21, 2022, 4:23 PM – Nov. 21, 2022, 4:36 PM

  Presenters

  • Xiantao Fan

    • University of Notre Dame

  Authors

  • Xiantao Fan

    • University of Notre Dame

  • Jian-Xun Wang

    • University of Notre Dame

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• Competitive physics-informed networks for high-accuracy solutions to Navier-Stokes problems

  ORAL

  Nov. 21, 2022, 4:36 PM – Nov. 21, 2022, 4:49 PM

  Publication: Zeng, Q., Bryngelson, S. H., & Schäfer, F. (2022). Competitive Physics Informed Networks. arXiv preprint arXiv:2204.11144.

  Presenters

  • Yash Kothari

    • Georgia Tech

  Authors

  • Yash Kothari

    • Georgia Tech

  • Qi Zeng

    • Georgia Tech

  • Florian Schaefer

    • Georgia Tech

  • Spencer H Bryngelson

    • Georgia Tech
    • Georgia Institute of Technology

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• β-Variational autoencoders for nonlinear and ortogonal reduced-order models in turbulence

  ORAL

  Nov. 21, 2022, 4:49 PM – Nov. 21, 2022, 5:02 PM

  Publication: Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows. H Eivazi, S Le Clainche, S Hoyas, R Vinuesa. Expert Systems with Applications 202, 117038, 2022

  Presenters

  • Ricardo Vinuesa

    • KTH
    • KTH Royal Institute of Technology
    • FLOW, KTH Engineering Mechanics

  Authors

  • Ricardo Vinuesa

    • KTH
    • KTH Royal Institute of Technology
    • FLOW, KTH Engineering Mechanics

  • Hamidreza Eivazi

    • KTH Royal Institute of Technology

  • Soledad Le Clainche

    • UPM

  • Sergio Hoyas

    • Univ Politecnica de Valencia

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• Accelerating Poisson equation solvers with physics informed neural networks

  ORAL

  Nov. 21, 2022, 5:02 PM – Nov. 21, 2022, 5:15 PM

  Presenters

  • Morgan Kerhouant

    • Imperial College London

  Authors

  • Morgan Kerhouant

    • Imperial College London

  • Thomas Abadie

    • Imperial College London; University of Birmingham
    • Department of Chemical Engineering, Imperial College London
    • Imperial College London; University of Birmingham, UK

  • Raj Venuturumilli

    • BP

  • Andre Nicolle

    • BP

  • Omar K Matar

    • Imperial College London
    • Imperial College London, The Alan Turing Institute

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• Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

  ORAL

  Nov. 21, 2022, 5:15 PM – Nov. 21, 2022, 5:28 PM

  Publication: https://gmd.copernicus.org/articles/15/3433/2022/

  Presenters

  • Romit Maulik

    • Argonne National Laboratory

  Authors

  • Romit Maulik

    • Argonne National Laboratory

  • Vishwas Rao

    • Argonne National Laboratory

  • Jiali Wang

    • Argonne National Laboratory

  • Gianmarco Mengaldo

    • National University of Singapore

  • Emil Constantinescu

    • Argonne National Laboratory

  • Bethany A Lusch

    • Argonne National Laboratory

  • Prasanna Balaprakash

    • Argonne National Laboratory

  • Ian Foster

    • Argonne National Laboratory

  • Rao Kotamarthi

    • Argonne National Lab
    • Argonne National Laboratory

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• Hydrokinetic turbine wake flow reconstruction in large-scale waterways using physics-informed convolutional neural networks

  ORAL

  Nov. 21, 2022, 5:28 PM – Nov. 21, 2022, 5:41 PM

  Presenters

  • Zexia Zhang

    • State University of New York at Stony Brook
    • Stony Brook University

  Authors

  • Zexia Zhang

    • State University of New York at Stony Brook
    • Stony Brook University

  • Ali Khosronejad

    • Stony Brook University

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• On the application of data-driven modeling within the rotorcraft design space

  ORAL

  Nov. 21, 2022, 5:41 PM – Nov. 21, 2022, 5:54 PM

  Presenters

  • Nicholas Peters

    • Embry-Riddle Aeronautical University-Wor

  Authors

  • Nicholas Peters

    • Embry-Riddle Aeronautical University-Wor

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• Efficient and Robust Training Strategies for Physics and Equality Constrained Artificial Neural Networks

  ORAL

  Nov. 21, 2022, 5:54 PM – Nov. 21, 2022, 6:07 PM

  Publication: Basir, S., & Senocak, I. (2022). Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion. Journal of Computational Physics, 111301.

  Presenters

  • shamsulhaq basir

    • University of Pittsburgh

  Authors

  • shamsulhaq basir

    • University of Pittsburgh

  • Inanc Senocak

    • University of Pittsburgh
    • University of Pittsburg

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• Prediction and Control of 2D Decaying Turbulence using Generative Adversarial Networks

  ORAL

  Nov. 21, 2022, 6:07 PM – Nov. 21, 2022, 6:20 PM

  Presenters

  • Jiyeon Kim

    • Yonsei University

  Authors

  • Jiyeon Kim

    • Yonsei University

  • Junhyuk Kim

    • Yonsei University

  • Changhoon Lee

    • Yonsei University

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• A heterogeneous computing approach to coupled simulation and machine-learning deployment for high-speed flows

  ORAL

  Nov. 21, 2022, 6:20 PM – Nov. 21, 2022, 6:33 PM

  Presenters

  • Charlelie Laurent

    • Center for Turbulence Research, Stanford University
    • Stanford University

  Authors

  • Charlelie Laurent

    • Center for Turbulence Research, Stanford University
    • Stanford University

  • Kazuki Maeda

    • Center for Turbulence Research, Stanford University
    • Center for Turbulence Research, Stanford University, CA, USA
    • Stanford University

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• Machine Learning Flux-Limiters for Compressible Flow Simulations

  ORAL

  Nov. 21, 2022, 6:33 PM – Nov. 21, 2022, 6:46 PM

  Presenters

  • Robert M Chiodi

    • Los Alamos National Laboratory

  Authors

  • Robert M Chiodi

    • Los Alamos National Laboratory

  • Nga T Nguyen-Fotiadis

    • Los Alamos National Laboratory

  • Michael McKerns

    • Los Alamos National Laboratory

  • Andrew T Sornborger

    • Los Alamos National Laboratory

  • Daniel Livescu

    • LANL
    • Los Alamos National Laboratory

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