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Semi Conditional Variational Auto-Encoder for Flow Reconstruction and
  Uncertainty Quantification from Limited Observations

Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations

19 July 2020
K. Gundersen
A. Oleynik
N. Blaser
G. Alendal
    BDL
ArXivPDFHTML

Papers citing "Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations"

5 / 5 papers shown
Title
SetPINNs: Set-based Physics-informed Neural Networks
SetPINNs: Set-based Physics-informed Neural Networks
M. Nagda
Phil Ostheimer
Thomas Specht
Frank Rhein
F. Jirasek
Stephan Mandt
Marius Kloft
Sophie Fellenz
3DPC
PINN
46
0
0
30 Sep 2024
Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
Farah Alsafadi
Aidan Furlong
Xu Wu
UQCV
AI4CE
39
3
0
09 Sep 2024
Generative Adversarial Reduced Order Modelling
Generative Adversarial Reduced Order Modelling
Dario Coscia
N. Demo
G. Rozza
GAN
AI4CE
39
5
0
25 May 2023
Ensemble flow reconstruction in the atmospheric boundary layer from
  spatially limited measurements through latent diffusion models
Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
A. Rybchuk
M. Hassanaly
N. Hamilton
P. Doubrawa
Mitchell J. Fulton
L. Martínez‐Tossas
AI4CE
DiffM
27
16
0
01 Mar 2023
Image features of a splashing drop on a solid surface extracted using a
  feedforward neural network
Image features of a splashing drop on a solid surface extracted using a feedforward neural network
Jingzu Yee
A. Yamanaka
Yoshiyuki Tagawa(田川義之)
19
14
0
24 Jan 2022
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