Bulletin of the American Physical Society

73rd Annual Meeting of the APS Division of Fluid Dynamics

Volume 65, Number 13

Sunday–Tuesday, November 22–24, 2020; Virtual, CT (Chicago time)

Session Index

Session R01: Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part I) (5:00pm - 5:45pm CST)

Interactive On Demand

Show Abstracts

Monday, November 23, 2020

		R01.00001: Pollution Transport Simulation and Machine-Learning Aided Source Detection in Metropolitan Areas Sarah Zhang Preview Abstract
		R01.00002: Unstructured fluid flow data recovery using machine learning and Voronoi diagrams Kai Fukami, Romit Maulik, Nesar Ramachandra, Kunihiko Taira, Koji Fukagata Preview Abstract
		R01.00003: Estimating model error using sparsity-promoting ensemble Kalman inversion Jinlong Wu, Tapio Schneider, Andrew Stuart Preview Abstract
		R01.00004: Deep Operator Neural Networks (DeepONets) for prediction of instability waves in high-speed boundary layers Patricio Clark Di Leoni, Charles Meneveau, George Karniadakis, Tamer Zaki Preview Abstract
		R01.00005: Active Learning of Nonlinear Operators for Forecasting Extreme and Rare Events Themistoklis Sapsis, George Karniadakis Preview Abstract
		R01.00006: Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database Michele Buzzicotti, Fabio Bonaccorso, Patricio Clark Di Leoni, Luca Biferale Preview Abstract
		R01.00007: Non-invasive Inference of Thrombus Material Properties with Physics-informed Neural Networks Minglang Yin, Xiaoning Zheng, Jay Humphrey, George Karniadakis Preview Abstract
		R01.00008: Application of a Machine Learning Turbulent and Non-turbulent Classification Method to Wall Modeled LES of Transitional Channel Flows Ghanesh Narasimhan, Charles Meneveau, Tamer Zaki Preview Abstract
		R01.00009: Learning high dimensional surrogates from mantle convection simulations Siddhant Agarwal, Nicola Tosi, Pan Kessel, Doris Breuer, Sebastiano Padovan, Grégoire Montavon Preview Abstract
		R01.00010: Deep Reinforcement Learning for Bluff Body Active Flow Control in Experiments and Simulations. Dixia Fan, Liu Yang, Zhicheng Wang, Michael Triantafyllou, George Karniadakis Preview Abstract
		R01.00011: Super-resolution and Denoising of Fluid Flows Using Physics-informed Convolutional Neural Networks Jian-Xun Wang, Han Gao, Luning Sun Preview Abstract
		R01.00012: Stable and Generalizable Subgrid Modeling of Forced Burgers Turbulence Using Neural Networks and Transfer Learning Adam Subel, Ashesh Chattopadhyay, Yifei Guan, Pedram Hassanzadeh Preview Abstract
		R01.00013: Statistically constrained neural networks for augmenting LES wall modeling Yue Hao, Charles Meneveau, Tamer Zaki Preview Abstract
		R01.00014: FiniteNet: A Fully Convolutional LSTM Network Architecture for Time-Dependent Partial Differential Equations Ben Stevens, Tim Colonius Preview Abstract
		R01.00015: Visualization of internal procedure in neural networks for fluid flows Masaki Morimoto, Kai Fukami, Koji Fukagata Preview Abstract
		R01.00016: Emulating turbulence via a Physics-Informed Deep Learning framework Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg Preview Abstract
		R01.00017: Autoencoded Reservoir Computing for the Spatio-Temporal Prediction of a Turbulent Flow Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri Preview Abstract
		R01.00018: Deep Reinforcement Learning for Efficient Navigation in Vortical Flow Fields Peter Gunnarson, Ioannis Mandralis, Guido Novati, Petros Koumoutsakos, John Dabiri Preview Abstract
		R01.00019: Convolutional neural network based wall modeling for large eddy simulation in a turbulent channel flow Naoki Moriya, Kai Fukami, Yusuke Nabae, Masaki Morimoto, Taichi Nakamura, Koji Fukagata Preview Abstract
		R01.00020: A data-driven wall model for LES of flow over periodic hills Zhideng Zhou, Guowei He, Xiaolei Yang Preview Abstract
		R01.00021: Machine learning method for 3D particle tracking velocimetry based on digital inline holography Jiarong Hong, Ruichen He, Siyao Shao, Kevin Mallery, Santosh Kumar Preview Abstract
		R01.00022: Modeling wall-shear stress of turbulent flows through deep reinforcement learning Junhyuk Kim, Hyojin Kim, Changhoon Lee Preview Abstract
		R01.00023: Super-resolution reconstruction of turbulence using unsupervised deep learning Hyojin Kim, Junhyuk Kim, Sungjin Won, Changhoon Lee Preview Abstract
		R01.00024: Data assimilation assisted neural network parameterizations for subgrid processes in multiscale systems Suraj Pawar, Omer San Preview Abstract
		R01.00025: Closed-loop optimal control for shear flows using reinforcement learning Onofro Semeraro, Michele Alessandro Bucci, Lionel Mathelin Preview Abstract
		R01.00026: Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning Ushnish Sengupta, Guenther Waxenegger-Wilfing, Jan Martin, Justin Hardi, Matthew Juniper Preview Abstract
		R01.00027: Estimation of 3D Velocity and Pressure Fields from Tomographic Background Oriented Schlieren Videos using a Physics-Informed Neural Network Shengze Cai, Zhicheng Wang, Frederik Fuest, Young Jin Jeon, Callum Gray, George Karniadakis Preview Abstract
		R01.00028: Convolutional neural networks to predict the onset of oscillatory instabilities in turbulent systems Eustaquio Aguilar Ruiz, Vishnu Rajasekharan Unni, R. I. Sujith, Abhishek Saha Preview Abstract
		R01.00029: Equivariance-preserving Deep Spatial Transformers for Auto-regressive Data-driven Forecasting of Geophysical Turbulence. Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath Preview Abstract
		R01.00030: Data-driven super-parameterization of subgrid-scale processes using deep learning Pedram Hassanzadeh, Ashesh Chattopadhyay, Adam Subel, Yifei Guan Preview Abstract
		R01.00031: Prediction of Rheological Parameters using Surrogate Models with Neural Networks James Hewett, Mathieu Sellier, Dale Cusack, Ben Kennedy, Miguel Moyers-Gonzalez, Jerome Monnier Preview Abstract
		R01.00032: Control by Deep Reinforcement Learning of a separated flow Thibaut Guegan, Michele Alessandro Bucci, Onofrio Semeraro, Laurent Cordier, Lionel Mathelin Preview Abstract
		R01.00033: Learning Full Flow Fields from Sparse Wind Tunnel Data Pablo Hermoso Moreno, Emile Oshima, Shengze Cai, Morteza Gharib Preview Abstract
		R01.00034: Interface learning paradigms for multi-scale and multi-physics systems Shady Ahmed, Suraj Pawar, Omer San Preview Abstract
		R01.00035: Robust Reservoir Computing for the Prediction of Chaotic Systems Alberto Racca, Luca Magri Preview Abstract
		R01.00036: A Deep Learning Framework for Computational Fluid Dynamics on Irregular Geometries Ali Kashefi, Davis Rempe, Leonidas Guibas Preview Abstract
Not Participating		R01.00037: Identifying Flow Physics in Convolutional Layers Ashley Scillitoe, Pranay Seshadri Preview Abstract
		R01.00038: A unifying framework of solving forward and inverse problems in fluid mechanics via deep learning Han Gao, Jian-Xun Wang Preview Abstract
		R01.00039: A Generative Model to Solve Steady Navier-Stokes Equations with Reduced Training Shen Wang, Joshua Agar, Yaling Liu Preview Abstract

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Bulletin of the American Physical Society

73rd Annual Meeting of the APS Division of Fluid Dynamics

Volume 65, Number 13

Sunday–Tuesday, November 22–24, 2020; Virtual, CT (Chicago time)

Session Index

Session R01: Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part I) (5:00pm - 5:45pm CST)

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