Neural networks in surrogate modeling
ORAL · T28 · ID: 1765447
Presentations
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Interpretable fine-tuning of graph neural network surrogates
ORAL
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Presenters
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Shivam Barwey
Argonne National Laboratory
Authors
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Shivam Barwey
Argonne National Laboratory
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Romit Maulik
Pennsylvania State University
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Estimation of oscillation parameters of a circular cylinder from its downstream vorticity fields
ORAL
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Presenters
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Hikaru Chida
Keio University
Authors
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Hikaru Chida
Keio University
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Kai Zhang
Shanghai Jiao Tong University
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Koji Fukagata
Keio University, Keio Univ
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Calibrating and optimizing operator terms via a neural Galerkin Projection on the Shallow Water Equations
ORAL
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Presenters
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Shane X Coffing
Los Alamos National Laboratory
Authors
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Shane X Coffing
Los Alamos National Laboratory
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Darren Engwirda
Los Alamos National Laboratory
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Rohit Kameshwara Sampath Sai K Vuppala
Oklahoma State University-Stillwater
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Arvind T Mohan
Los Alamos National Laboratory
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Reservoir computing of thermal convection: Random versus small-world networks
ORAL
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Presenters
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Shailendra K Rathor
Technische Universität Ilmenau
Authors
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Shailendra K Rathor
Technische Universität Ilmenau
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Joerg Schumacher
Technische Universität Ilmenau, TU Ilmenau
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Input-Output turbulent flow models using Fourier Neural Operators
ORAL
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Presenters
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Federico Rios Tascon
Stanford University
Authors
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Federico Rios Tascon
Stanford University
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Aakash Patil
Stanford University
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Peter J Schmid
King Abdullah University of Science and Technology
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Beverley J McKeon
Stanford University
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Analysis of air-jet vortex-generator controlled SWBLI using deep encoder-decoder convolutional network
ORAL
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Presenters
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Robin Sebastian
RWTH Aachen University
Authors
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Robin Sebastian
RWTH Aachen University
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Saahith Velivolu
FH Aachen
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Anne-Marie Schreyer
RWTH Aachen University
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Reduced-Order Modelling of Stochastically Forced Zonal Jets using a 'Stochastic Latent Transformer'
ORAL
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Publication: Shokar, I., Haynes, P., and Kerswell, R.: Learning Stochastic Dynamics with Probabilistic Neural Networks to study Zonal Jets, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9121, https://doi.org/10.5194/egusphere-egu23-9121, 2023.
Presenters
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Ira J Shokar
DAMTP, University of Cambridge
Authors
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Ira J Shokar
DAMTP, University of Cambridge
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Peter H Haynes
DAMTP, University of Cambridge, University of Cambridge
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Rich R Kerswell
Univ of Cambridge, DAMTP, University of Cambridge
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Volumetric flow rate prediction of disturbed pipe flow based on single path velocity data using a shallow neural network
ORAL
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Presenters
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Christoph Wilms
Physikalisch-Technische Bundesanstalt
Authors
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Christoph Wilms
Physikalisch-Technische Bundesanstalt
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Ann-Kathrin Ekat
Physikalisch-Technische Bundesanstalt
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Katja Hertha-Dunkel
Physikalisch-Technische Bundesanstalt
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Thomas Eichler
Physikalisch-Technische Bundesanstalt
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Sonja Schmelter
Physikalisch-Technische Bundesanstalt
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