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Statistical treatment of convolutional neural network super-resolution
  of inland surface wind for subgrid-scale variability quantification

Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification

30 November 2022
Daniel J. Getter
J. Bessac
J. Rudi
Yan Feng
ArXivPDFHTML

Papers citing "Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification"

3 / 3 papers shown
Title
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
Rupa Kurinchi-Vendhan
Björn Lütjens
Ritwik Gupta
Lucien Werner
Dava Newman
18
17
0
17 Sep 2021
Machine Learning for Stochastic Parameterization: Generative Adversarial
  Networks in the Lorenz '96 Model
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
D. Gagne
H. Christensen
A. Subramanian
A. Monahan
AI4CE
BDL
41
139
0
10 Sep 2019
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
195
5,175
0
16 Sep 2016
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