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Data-driven Nonlinear Parametric Model Order Reduction Framework using
  Deep Hierarchical Variational Autoencoder

Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder

10 July 2023
Sihun Lee
Sangmin Lee
Ki-Hyun Jang
Haeseong Cho
Sang-Joon Shin
ArXivPDFHTML

Papers citing "Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder"

10 / 10 papers shown
Title
$β$-Variational autoencoders and transformers for reduced-order
  modelling of fluid flows
βββ-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Alberto Solera-Rico
Carlos Sanmiguel Vila
Miguel Gómez-López
Yuning Wang
Abdulrahman Almashjary
Scott T. M. Dawson
Ricardo Vinuesa
DRL
38
81
0
07 Apr 2023
Physics-aware Reduced-order Modeling of Transonic Flow via
  $β$-Variational Autoencoder
Physics-aware Reduced-order Modeling of Transonic Flow via βββ-Variational Autoencoder
Yu-Eop Kang
Sunwoong Yang
K. Yee
DRL
AI4CE
43
25
0
02 May 2022
Towards extraction of orthogonal and parsimonious non-linear modes from
  turbulent flows
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi
S. L. C. Martínez
S. Hoyas
Ricardo Vinuesa
56
92
0
03 Sep 2021
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
54
61
0
23 Jul 2021
A fast and accurate physics-informed neural network reduced order model
  with shallow masked autoencoder
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
Youngkyu Kim
Youngsoo Choi
David Widemann
T. Zohdi
AI4CE
34
191
0
25 Sep 2020
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady
  Flows
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
Hamidreza Eivazi
H. Veisi
M. H. Naderi
V. Esfahanian
AI4CE
49
169
0
02 Jul 2020
Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss
  Function
Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
Stephen G. Odaibo
GAN
BDL
DRL
25
54
0
21 Jul 2019
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL
  Vanishing
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Hao Fu
Chunyuan Li
Xiaodong Liu
Jianfeng Gao
Asli Celikyilmaz
Lawrence Carin
ODL
63
363
0
25 Mar 2019
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
209
5,502
0
23 Nov 2015
Generating Sentences from a Continuous Space
Generating Sentences from a Continuous Space
Samuel R. Bowman
Luke Vilnis
Oriol Vinyals
Andrew M. Dai
Rafal Jozefowicz
Samy Bengio
DRL
77
2,352
0
19 Nov 2015
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