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 S01: Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part II) (5:45pm - 6:30pm CST)

Interactive On Demand

Show Abstracts

Monday, November 23, 2020

		S01.00001: Super-resolution of Finite Element spaces using Physics-informed Deep Learning Networks for Turbulent flows Aniruddhe Pradhan, Rajarshi Biswas, Karthik Duraisamy Preview Abstract
		S01.00002: Dispersed Multiphase Flow Generation using 3D Steerable Convolutional Neural Network Bhargav Sriram Siddani, S. Balachandar, Ruogu Fang, William Chandler Moore, Yunchao Yang Preview Abstract
		S01.00003: Deep Reinforcement Learning for Control of Fuel Injection in Compression Ignition Engines Nicholas Wimer, Marc Henry de Frahan, Shashank Yellapantula, Ray Grout Preview Abstract
		S01.00004: Performance Bounds of Data-Driven Reynolds Stress Models via Optimal Tensor Basis Expansions Andrew J. Banko, Christopher J. Elkins, John K. Eaton Preview Abstract
		S01.00005: Using generative adversarial networks for subfilter modeling of turbulent flows Mathis Bode Preview Abstract
		S01.00006: Predictions in Wall-bounded Turbulence Through Convolutional-network Models Using Wall Quantities Luca Guastoni, Alejandro G\"uemes, Andrea Ianiro, Stefano Discetti, Philipp Schlatter, Hossein Azizpour, Ricardo Vinuesa Preview Abstract
		S01.00007: SimNet: A neural network solver for multi-Physics applications Oliver Hennigh, Kaustubh Tangsali, Akshay Subramaniam, Susheela Narasimhan, Mohammad Nabian, Jose del Aguila Ferrandis, Sanjay Choudhry Preview Abstract
		S01.00008: Generalization of Machine Learning Criteria for Ignition Prediction Faustino Martinez, Pavel Popov Preview Abstract
		S01.00009: Learning minimal representations for chaotic dynamics of partial differential equations Alec J. Linot, Michael D. Graham Preview Abstract
		S01.00010: Flowtaxis in the wakes of oscillating airfoils Haotian Hang, Sina Heydari, Brendan Colvert, Eva Kanso Preview Abstract
		S01.00011: Physics-informed Autoencoders for Operator-theoretic decomposition and Model reduction of Complex Flows Karthik Duraisamy, Shaowu Pan Preview Abstract
		S01.00012: Fast solver of the shallow water equations with application to estimation of the riverine surface flow velocity Mojtaba Forghani, Yizhou Qian, Peter Kitanidis, Matthew Farthing, Tyler Hesser, Jonghyun Lee, Eric Darve Preview Abstract
		S01.00013: Deep learning to predict the effectiveness factor in the closure problems Ehsan Taghizadeh, Paul Macklin, Helen Byrne, Brian Wood Preview Abstract
		S01.00014: Embedded training of neural-network sub-grid-scale turbulence models Justin Sirignano, Jonathan MacArt, Jonathan Freund Preview Abstract
		S01.00015: Deep learning-based assignment of combustion submodels for large-eddy simulation Wai Tong Chung, Aashwin Mishra, Nikolaos Perakis, Matthias Ihme Preview Abstract
		S01.00016: Embedding Physics as Hard Constraints in Generative Adversarial Networks for 3D Turbulence Dima Tretiak, Arvind Mohan, Daniel Livescu Preview Abstract
		S01.00017: Dynamic Masking of PIV Images using Deep Learning Bernhard Vennemann, Thomas Rösgen Preview Abstract
		S01.00018: Pollution Source Localization Using Physics-Driven Deep Neural Net Roshan D'Souza, Isaac Perez-Raya Preview Abstract
		S01.00019: LES Turbulence Model with Learnt Closure; Integration of DNN into a CFD Solver Majid Haghshenas, Peetak Mitra, Niccolo Dal Santo, Mateus Dias Ribeiro, Shounak Mitra, David Schmidt Preview Abstract
		S01.00020: Deep learning-based shadowgraph: implementation of Mask R-CNN to bubble detection in complex two-phase Yewon Kim, Hyungmin Park Preview Abstract
		S01.00021: A hybrid data-driven deep learning technique for fluid-structure interaction Rajeev Jaiman, Tharindu Miyanawala Preview Abstract
		S01.00022: Learning to write and paint using a liquid rope trick Gaurav Chaudhary, Stephanie Christ, A. John Hart, L. Mahadevan Preview Abstract
		S01.00023: Inference on spatially unstructured flow fields using Graph Neural Networks Francis Ogoke, Kazem Meidani, Amirreza Hashemi, Amir Barati Farimani Preview Abstract
		S01.00024: Machine Learning Statistical Lagrangian Geometry of Turbulence Criston Hyett, Michael Chertkov, Yifeng Tian, Daniel Livescu Preview Abstract
		S01.00025: Modeling active fluids via physically constrained machine learning Matthew Golden, Jyothishraj Nambisan, Alberto Fernandez-Nieves, Roman Grigoriev Preview Abstract
		S01.00026: Reconstruction of Turbulent High-resolution DNS Data Using Deep Learning Pranshu Pant, Amir Barati Farimani Preview Abstract
		S01.00027: Machine Learning of Reduced Lagrangian Models of Turbulence Michael Woodward, Yifeng Tian, Michael Chertkov, Mikhail Stepanov, Daniel Livescu, Chris Fryer Preview Abstract
		S01.00028: Physics-informed Machine Learning of the Lagrangian Dynamics of Velocity Gradient Tensor Yifeng Tian, Daniel Livescu, Michael Chertkov Preview Abstract
		S01.00029: Learning Physics-based Galerkin models of turbulence with Neural Differential Equations Arvind Mohan, Kaushik Nagarajan, Daniel Livescu Preview Abstract
		S01.00030: Designing networks to accurately learn 2D turbulence closures Keaton Burns, Ronan Legin, Adrian Liu, Laurence Perreault-Levasseur, Yashar Hezaveh, Siamak Ravanbakhsh, Gregory Wagner Preview Abstract
		S01.00031: Turbulence closure modeling with machine-learning methods: Influence of choice of neural network and training procedure Salar Taghizadeh, Yassin Hassan, Freddie Witherden, Sharath Girimaji Preview Abstract
		S01.00032: Turbulence closure modeling with Machine-Learning Methods: Can RANS overcome curse of averaging? Sharath Girimaji Preview Abstract
		S01.00033: A Deep Learning Based Physics Informed Continuous Spatio Temporal Super-Resolution Framework Soheil Esmaeilzadeh, Chiyu Max Jiang, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Mr Prabhat, Anima Anandkumar Preview Abstract
		S01.00034: Developing an automatic calibration tool for turbulence closure models using machine learning techniques Ismael Boureima, Vitaliy Gyrya, Juan Saenz, Susan Kurien 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 S01: Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part II) (5:45pm - 6:30pm CST)

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