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Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders
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Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders

Stefanos Nikolopoulos
I. Kalogeris
V. Papadopoulos
ArXiv (abs)PDFHTML

Papers citing "Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders"

3 / 3 papers shown
Title
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task
Sunwoong Yang
K. Yee
UQCV
88
6
0
28 Mar 2023
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
T. Kadeethum
F. Ballarin
Y. Cho
Daniel O’Malley
H. Yoon
N. Bouklas
AI4CE
92
61
0
23 Jul 2021
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
Vinicius L. S. Silva
Claire E. Heaney
Yaqi Li
Christopher C. Pain
AI4CE
64
23
0
17 May 2021

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