CFD: Uncertainty Quantification and Machine Learning
ORAL · A28 · ID: 678147
Presentations
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Data assimilation using particle filters in reduced-order model subspaces
ORAL
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Publication: Albarakati, Aishah, Marko Budišić, Rose Crocker, Juniper Glass-Klaiber, Sarah Iams, John Maclean, Noah Marshall, Colin Roberts, and Erik S. Van Vleck. 2021. "Model and Data Reduction for Data Assimilation: Particle Filters Employing Projected Forecasts and Data with Application to a Shallow Water Model." Computers & Mathematics with Applications, June. https://doi.org/10.1016/j.camwa.2021.05.026.<br>
Presenters
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Marko Budisic
Clarkson University
Authors
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Aishah Albarakati
Clarkson University
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Marko Budisic
Clarkson University
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Colin Roberts
Colorado State University
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Erik S Van Vleck
University of Kansas
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Data-assisted uncertainty quantification and extreme event prediction in climate models using physically-consistent neural networks.
ORAL
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Presenters
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Alexis-Tzianni Charalampopoulos
Massachusetts Institute of Technology MI
Authors
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Alexis-Tzianni Charalampopoulos
Massachusetts Institute of Technology MI
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Shixuan Zhang
Pacific Northwest National Laboratory
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Ruby Leung
Pacific Northwest National Laboratory
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Themistoklis Sapsis
Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology MI
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Extracting Navier-Stokes solutions from noisy data with physics-constrained convolutional neural networks
ORAL
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Presenters
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Luca Magri
Imperial College London; Alan Turing Institute, Department of Aeronautics, Imperial College London; The Alan Turing Institute, Imperial College London, The Alan Turing Institute, Imperial College London, Imperial College London; The Alan Turing Institute, Imperial College London, Alan Turing Institute
Authors
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Daniel Kelshaw
Imperial College London
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Luca Magri
Imperial College London; Alan Turing Institute, Department of Aeronautics, Imperial College London; The Alan Turing Institute, Imperial College London, The Alan Turing Institute, Imperial College London, Imperial College London; The Alan Turing Institute, Imperial College London, Alan Turing Institute
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Hierarchical Bayesian multifidelity modelling applied to turbulent flows
ORAL
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Presenters
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Philipp Schlatter
KTH, FLOW, KTH Engineering Mechanics, KTH Engineering Mechanics, Royal Institute of Technology, KTH Engineering Mechanics
Authors
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Philipp Schlatter
KTH, FLOW, KTH Engineering Mechanics, KTH Engineering Mechanics, Royal Institute of Technology, KTH Engineering Mechanics
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Saleh Rezaeiravesh
KTH Engineering Mechanics
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Timofey Mukha
KTH Engineering Mechanics
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Probabilistic surrogate modeling of unsteady fluid dynamics using deep graph normalizing flows
ORAL
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Presenters
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Luning Sun
University of Notre Dame
Authors
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Luning Sun
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Neural networks for multi-fidelity ensemble large-eddy simulations
ORAL
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Presenters
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Mark Benjamin
Department of Mechanical Engineering, Stanford University
Authors
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Mark Benjamin
Department of Mechanical Engineering, Stanford University
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Stefan P Domino
Institute for Computational and Mathematical Engineering, Stanford University
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Gianluca Iaccarino
Mechanical Engineering Department, Stanford University, Stanford University, Department of Mechanical Engineering, Stanford University
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Quantifying uncertainty in large-eddy simulation results of a natural river flow
ORAL
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Presenters
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Kevin Flora
Stony Brook University
Authors
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Kevin Flora
Stony Brook University
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Ali Khosronejad
Stony Brook University
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Uncertainty Propagation in CFD Simulations using Non-Intrusive Polynomial Chaos Expansion and Reduced Order Modeling
ORAL
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Presenters
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Nikhil Iyengar
Georgia Institute of Technology
Authors
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Nikhil Iyengar
Georgia Institute of Technology
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Dimitri Mavris
Georgia Institute of Technology
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Dushhyanth Rajaram
Georgia Institute of Technology
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RoseNNa: A performant library for portable neural network inference with application to CFD
ORAL
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Presenters
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Ajay Bati
Georgia Tech
Authors
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Ajay Bati
Georgia Tech
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Spencer H Bryngelson
Georgia Tech, Georgia Institute of Technology
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