Modeling Methods I: Closure Models, Automated Discovery of Equations, and Prediction
ORAL · R29 · ID: 1765410
Presentations
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Closure modeling through the lens of multifidelity operator learning
ORAL
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Publication: Ahmed, S. E., & Stinis, P. (2023). A multifidelity deep operator network approach to closure for multiscale systems. Computer Methods in Applied Mechanics and Engineering, 414, 116161.
Presenters
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Shady E Ahmed
Pacific Northwest National Laboratory
Authors
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Shady E Ahmed
Pacific Northwest National Laboratory
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Panos Stinis
Pacific Northwest National Laboratory
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A Closed Machine Learning Parametric Reduced Order Model Approach - Application to Turbulent Flows
ORAL
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Publication: https://arxiv.org/pdf/2304.14183.pdf
Presenters
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Rama AYOUB
King Abdullah Univ of Sci & Tech (KAUST)
Authors
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Rama AYOUB
King Abdullah Univ of Sci & Tech (KAUST)
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Mourad Oulghelou
ENSAM
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Amine AMMAR
ENSAM
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Discovery of viscoelastic constitutive models with complexity-penalized sparse regression
ORAL
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Presenters
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Sarah Beetham
Oakland University
Authors
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Sarah Beetham
Oakland University
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Konstantinos Zinelis
Imperial College London
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Thomas Abadie
Department of Chemical Engineering, Imperial College London
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Gareth H McKinley
Massachusetts Institute of Technology
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Omar K Matar
Imperial College London
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Jesse Capecelatro
University of Michigan
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Neural Operator for Modeling Dynamical Systems with Trajectories and Statistics Matching
ORAL
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Presenters
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Chuanqi Chen
University of Wisconsin - Madison
Authors
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Chuanqi Chen
University of Wisconsin - Madison
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Jinlong Wu
University of Wisconsin-Madison, University of Wisconsin - Madison, University of Wisconsin–Madison
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Bayesian Identification of Nonlinear Dynamics (BINDy)
ORAL
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Presenters
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Lloyd Fung
Univ of Cambridge
Authors
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Lloyd Fung
Univ of Cambridge
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Urban Fasel
Imperial College London
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Matthew P Juniper
Univ of Cambridge
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Application of Denoising Diffusion Probabilistic Models to Turbulence Prediction
ORAL
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Presenters
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Jiyeon Kim
Yonsei University
Authors
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Jiyeon Kim
Yonsei University
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Changhoon Lee
Yonsei University
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Data-driven observable discovery for reduced-order modeling of turbulence based on the Mori-Zwanzig formalism
ORAL
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Presenters
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Joel Barnett
University of California, Los Angeles
Authors
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Yifeng Tian
Los Alamos National Laboratory
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Joel Barnett
University of California, Los Angeles
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Yen Ting Lin
Los Alamos National Labs
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Daniel Livescu
LANL
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An improved likelihood-weighted sequential sampling method for extreme events statistics
ORAL
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Presenters
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Xianliang Gong
University of Michigan
Authors
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Xianliang Gong
University of Michigan
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Yulin Pan
University of Michigan
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