Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part I) (5:00pm - 5:45pm CST)

Presentations

• Pollution Transport Simulation and Machine-Learning Aided Source Detection in Metropolitan Areas

  POSTER

  Authors

  • Sarah Zhang

    • Thomas Jefferson High School for Science and Technology

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• Unstructured fluid flow data recovery using machine learning and Voronoi diagrams

  POSTER

  Authors

  • Kai Fukami

    • Keio University

  • Romit Maulik

    • Argonne National Laboratory

  • Nesar Ramachandra

    • Argonne National Laboratory

  • Kunihiko Taira

    • University of California - Los Angeles
    • University of California, Los Angeles
    • Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA
    • UCLA
    • University of California Los Angeles

  • Koji Fukagata

    • Keio University

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• Estimating model error using sparsity-promoting ensemble Kalman inversion

  POSTER

  Authors

  • Jinlong Wu

    • California Institute of Technology

  • Tapio Schneider

    • California Institute of Technology

  • Andrew Stuart

    • California Institute of Technology

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• Deep Operator Neural Networks (DeepONets) for prediction of instability waves in high-speed boundary layers

  POSTER

  Authors

  • Patricio Clark Di Leoni

    • Department of Mechanical Engineering, Johns Hopkins University
    • Johns Hopkins University

  • Charles Meneveau

    • Department of Mechanical Engineering, John Hopkins University,USA
    • Johns Hopkins University

  • George Karniadakis

    • Center for Fluid Mechanics, Brown University, USA
    • Brown University

  • Tamer Zaki

    • Johns Hopkins University
    • Department of Mechanical Engineering, John Hopkins University,USA

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• Active Learning of Nonlinear Operators for Forecasting Extreme and Rare Events

  POSTER

  Authors

  • Themistoklis Sapsis

    • Massachusetts Institute of Technology MIT
    • MIT

  • George Karniadakis

    • Center for Fluid Mechanics, Brown University, USA
    • Brown University

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• Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database

  POSTER

  Authors

  • Michele Buzzicotti

    • Department of Physics and INFN, University of Rome Tor Vergata

  • Fabio Bonaccorso

    • Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia and INFN

  • Patricio Clark Di Leoni

    • Department of Mechanical Engineering, Johns Hopkins University
    • Johns Hopkins University

  • Luca Biferale

    • Department of Physics and INFN, University of Rome Tor Vergata

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• Non-invasive Inference of Thrombus Material Properties with Physics-informed Neural Networks

  POSTER

  Authors

  • Minglang Yin

    • Brown University

  • Xiaoning Zheng

    • Brown University

  • Jay Humphrey

    • Yale University

  • George Karniadakis

    • Brown University

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• Application of a Machine Learning Turbulent and Non-turbulent Classification Method to Wall Modeled LES of Transitional Channel Flows

  POSTER

  Authors

  • Ghanesh Narasimhan

    • Johns Hopkins University

  • Charles Meneveau

    • Johns Hopkins University

  • Tamer Zaki

    • Johns Hopkins University

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• Learning high dimensional surrogates from mantle convection simulations

  POSTER

  Authors

  • Siddhant Agarwal

    • German Aerospace Center (DLR)

  • Nicola Tosi

    • German Aerospace Center (DLR)

  • Pan Kessel

    • Technical University Berlin

  • Doris Breuer

    • German Aerospace Center (DLR)

  • Sebastiano Padovan

    • German Aerospace Center (DLR)

  • Grégoire Montavon

    • Technical University Berlin

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• Deep Reinforcement Learning for Bluff Body Active Flow Control in Experiments and Simulations.

  POSTER

  Authors

  • Dixia Fan

    • Massachusetts Institute of Technology

  • Liu Yang

    • Brown University

  • Zhicheng Wang

    • Brown University

  • Michael Triantafyllou

    • Massachusetts Institute of Technology

  • George Karniadakis

    • Center for Fluid Mechanics, Brown University, USA
    • Brown University

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• Super-resolution and Denoising of Fluid Flows Using Physics-informed Convolutional Neural Networks

  POSTER

  Authors

  • Jian-Xun Wang

    • University of Notre Dame

  • Han Gao

    • University of Notre Dame

  • Luning Sun

    • University of Notre Dame

  View abstract →

• Stable and Generalizable Subgrid Modeling of Forced Burgers Turbulence Using Neural Networks and Transfer Learning

  POSTER

  Authors

  • Adam Subel

    • Rice University
    • Rice Univ

  • Ashesh Chattopadhyay

    • Rice Univ
    • Rice University

  • Yifei Guan

    • Rice University

  • Pedram Hassanzadeh

    • Rice University

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• Statistically constrained neural networks for augmenting LES wall modeling

  POSTER

  Authors

  • Yue Hao

    • Johns Hopkins University

  • Charles Meneveau

    • Department of Mechanical Engineering, John Hopkins University,USA
    • Johns Hopkins University

  • Tamer Zaki

    • Johns Hopkins University
    • Department of Mechanical Engineering, John Hopkins University,USA

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• FiniteNet: A Fully Convolutional LSTM Network Architecture for Time-Dependent Partial Differential Equations

  POSTER

  Authors

  • Ben Stevens

    • Caltech

  • Tim Colonius

    • Caltech

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• Visualization of internal procedure in neural networks for fluid flows

  POSTER

  Authors

  • Masaki Morimoto

    • Keio University

  • Kai Fukami

    • University of California, Los Angeles

  • Koji Fukagata

    • Keio University

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• Emulating turbulence via a Physics-Informed Deep Learning framework

  POSTER

  Authors

  • Mohammadreza Momenifar

    • Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina

  • Enmao Diao

    • Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina

  • Vahid Tarokh

    • Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina

  • Andrew Bragg

    • Duke University
    • Duke University, NC, USA
    • Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina

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• Autoencoded Reservoir Computing for the Spatio-Temporal Prediction of a Turbulent Flow

  POSTER

  Authors

  • Nguyen Anh Khoa Doan

    • Technical University of Munich

  • Wolfgang Polifke

    • Technical University of Munich

  • Luca Magri

    • University of Cambridge

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• Deep Reinforcement Learning for Efficient Navigation in Vortical Flow Fields

  POSTER

  Authors

  • Peter Gunnarson

    • California Institute of Technology

  • Ioannis Mandralis

    • ETH Zurich

  • Guido Novati

    • ETH Zurich

  • Petros Koumoutsakos

    • ETH Zurich

  • John Dabiri

    • California Institute of Technology
    • Caltech

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• Convolutional neural network based wall modeling for large eddy simulation in a turbulent channel flow

  POSTER

  Authors

  • Naoki Moriya

    • Keio University

  • Kai Fukami

    • University of California, Los Angeles

  • Yusuke Nabae

    • Keio University

  • Masaki Morimoto

    • Keio University

  • Taichi Nakamura

    • Keio University

  • Koji Fukagata

    • Keio University

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• A data-driven wall model for LES of flow over periodic hills

  POSTER

  Authors

  • Zhideng Zhou

    • The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences

  • Guowei He

    • The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences
    • Chinese Academy of Science

  • Xiaolei Yang

    • Institute of Mechanics, Chinese Academy of Sciences
    • The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences

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• Machine learning method for 3D particle tracking velocimetry based on digital inline holography

  POSTER

  Authors

  • Jiarong Hong

    • Department of Mechanical Engineering & St. Anthony Falls Laboratory, University of Minnesota
    • University of Minnesota
    • University of Minneasota
    • Saint Anthony Falls Laboratory, 2 3rd AVE SE, University of Minnesota, Minneapolis, MN, USA 55414

  • Ruichen He

    • University of Minnesota

  • Siyao Shao

    • University of Minnesota

  • Kevin Mallery

    • University of Minnesota

  • Santosh Kumar

    • University of Minnesota

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• Modeling wall-shear stress of turbulent flows through deep reinforcement learning

  POSTER

  Authors

  • Junhyuk Kim

    • Yonsei University

  • Hyojin Kim

    • Yonsei University

  • Changhoon Lee

    • Yonsei University

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• Super-resolution reconstruction of turbulence using unsupervised deep learning

  POSTER

  Authors

  • Hyojin Kim

    • Yonsei University

  • Junhyuk Kim

    • Yonsei University

  • Sungjin Won

    • Yonsei University

  • Changhoon Lee

    • Yonsei University

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• Data assimilation assisted neural network parameterizations for subgrid processes in multiscale systems

  POSTER

  Authors

  • Suraj Pawar

    • Oklahoma State University-Stillwater

  • Omer San

    • Oklahoma State University-Stillwater

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• Closed-loop optimal control for shear flows using reinforcement learning

  POSTER

  Authors

  • Onofrio Semeraro

    • CNRS - Universite Paris Saclay
    • LIMSI, CNRS, Universite' de Paris-Saclay
    • LIMSI-CNRS

  • Michele Alessandro Bucci

    • INRIA Saclay, France
    • TAU-Team, INRIA Saclay, LRI, Universite' Paris-Sud
    • INRIA-Saclay

  • Lionel Mathelin

    • CNRS - Universite Paris Saclay
    • LIMSI, CNRS, Universite' de Paris-Saclay
    • LIMSI-CNRS

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• Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning

  POSTER

  Authors

  • Ushnish Sengupta

    • University of Cambridge
    • Univ of Cambridge

  • Guenther Waxenegger-Wilfing

    • German Aerospace Center (DLR) Lampoldshausen

  • Jan Martin

    • German Aerospace Center (DLR) Lampoldshausen

  • Justin Hardi

    • German Aerospace Center (DLR) Lampoldshausen

  • Matthew Juniper

    • University of Cambridge
    • Univ of Cambridge
    • Department of Engineering, University of Cambridge

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• Estimation of 3D Velocity and Pressure Fields from Tomographic Background Oriented Schlieren Videos using a Physics-Informed Neural Network

  POSTER

  Authors

  • Shengze Cai

    • Brown University

  • Zhicheng Wang

    • Brown University

  • Frederik Fuest

    • LaVision GmbH

  • Young Jin Jeon

    • LaVision GmbH

  • Callum Gray

    • LaVision Inc.

  • George Karniadakis

    • Center for Fluid Mechanics, Brown University, USA
    • Brown University

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• Convolutional neural networks to predict the onset of oscillatory instabilities in turbulent systems

  POSTER

  Authors

  • Eustaquio Aguilar Ruiz

    • University of California San Diego

  • Vishnu Rajasekharan Unni

    • University of California San Diego

  • Raman I. Sujith

    • Indian Institute of Technology Madras
    • Indian Institute of Technology - Madras

  • Abhishek Saha

    • University of California San Diego

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• Equivariance-preserving Deep Spatial Transformers for Auto-regressive Data-driven Forecasting of Geophysical Turbulence.

  POSTER

  Authors

  • Ashesh Chattopadhyay

    • Rice Univ
    • Rice University

  • Mustafa Mustafa

    • Lawrence Berkeley National Laboratory

  • Pedram Hassanzadeh

    • Rice University

  • Karthik Kashinath

    • Lawrence Berkeley National Laboratory

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• Data-driven super-parameterization of subgrid-scale processes using deep learning

  POSTER

  Authors

  • Pedram Hassanzadeh

    • Rice Univ

  • Ashesh Chattopadhyay

    • Rice Univ

  • Adam Subel

    • Rice University
    • Rice Univ

  • Yifei Guan

    • Rice University
    • Rice Univ

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• Prediction of Rheological Parameters using Surrogate Models with Neural Networks

  POSTER

  Authors

  • James Hewett

    • University of Canterbury

  • Mathieu Sellier

    • University of Canterbury

  • Dale Cusack

    • University of Canterbury

  • Ben Kennedy

    • University of Canterbury

  • Miguel Moyers-Gonzalez

    • University of Canterbury

  • Jerome Monnier

    • INSA Toulouse

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• Control by Deep Reinforcement Learning of a separated flow

  POSTER

  Authors

  • Thibaut Guegan

    • Institut Pprime (CNRS, Universite de Poitiers, ISAE-ENSMA), France

  • Michele Alessandro Bucci

    • INRIA Saclay, France
    • TAU-Team, INRIA Saclay, LRI, Universite' Paris-Sud
    • INRIA-Saclay

  • Onofrio Semeraro

    • CNRS - Universite Paris Saclay
    • LIMSI, CNRS, Universite' de Paris-Saclay
    • LIMSI-CNRS

  • Laurent Cordier

    • Institut Pprime (CNRS, Universite de Poitiers, ISAE-ENSMA), France

  • Lionel Mathelin

    • CNRS - Universite Paris Saclay
    • LIMSI, CNRS, Universite' de Paris-Saclay
    • LIMSI-CNRS

  View abstract →

• Learning Full Flow Fields from Sparse Wind Tunnel Data

  POSTER

  Authors

  • Pablo Hermoso Moreno

    • Caltech

  • Emile Oshima

    • Caltech

  • Shengze Cai

    • Brown University

  • Morteza Gharib

    • Caltech
    • California Institute of Technology

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• Interface learning paradigms for multi-scale and multi-physics systems

  POSTER

  Authors

  • Shady Ahmed

    • Oklahoma State University-Stillwater

  • Suraj Pawar

    • Oklahoma State University-Stillwater

  • Omer San

    • Oklahoma State University-Stillwater

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• Robust Reservoir Computing for the Prediction of Chaotic Systems

  POSTER

  Authors

  • Alberto Racca

    • University of Cambridge

  • Luca Magri

    • University of Cambridge

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• A Deep Learning Framework for Computational Fluid Dynamics on Irregular Geometries

  POSTER

  Authors

  • Ali Kashefi

    • Department of Civil and Environmental Engineering, Stanford University

  • Davis Rempe

    • Department of Computer Science, Stanford University

  • Leonidas Guibas

    • Department of Computer Science, Stanford University

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• Identifying Flow Physics in Convolutional Layers

  POSTER

  Authors

  • Ashley Scillitoe

    • The Alan Turing Institute

  • Pranay Seshadri

    • Imperial College London

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• A unifying framework of solving forward and inverse problems in fluid mechanics via deep learning

  POSTER

  Authors

  • Han Gao

    • University of Notre Dame

  • Jian-Xun Wang

    • University of Notre Dame

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• A Generative Model to Solve Steady Navier-Stokes Equations with Reduced Training

  POSTER

  Authors

  • Shen Wang

    • Lehigh University

  • Joshua Agar

    • Lehigh University

  • Yaling Liu

    • Lehigh University
    • Lehigh Univ

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