scholarly journals High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network

2021 ◽  
Vol 33 (12) ◽  
pp. 125119
Author(s):  
Mustafa Z. Yousif ◽  
Linqi Yu ◽  
Hee-Chang Lim
Keyword(s):  
Turbulent Flow ◽  
Super Resolution ◽  
High Fidelity ◽  
Limited Data ◽  
Generative Adversarial Network ◽  
Adversarial Network

Super-resolution of low-fidelity flow solutions via generative adversarial networks

SIMULATION ◽  
2021 ◽  
pp. 003754972110612
Author(s):  
Mahdi Pourbagian ◽  
Ali Ashrafizadeh
Keyword(s):  
Incompressible Flows ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
High Fidelity ◽  
Generative Adversarial Network ◽  
Flow Problems ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
High Fidelity Simulations ◽  
Computational Resources

While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.

Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

2021 ◽  
Vol 58 (8) ◽  
pp. 0810005
Author(s):  
查体博 Zha Tibo ◽  
罗林 Luo Lin ◽  
杨凯 Yang Kai ◽  
张渝 Zhang Yu ◽  
李金龙 Li Jinlong
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Generative Adversarial Network ◽  
Image Reconstruction Algorithm ◽  
Adversarial Network

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

Infrared imagery super-resolution by using a generative adversarial network

2021 ◽  
Author(s):  
Trong-An Bui ◽  
Pei-Jun Lee ◽  
Kuan-Min Lee ◽  
Walter Wang ◽  
Shiual-Hal Shiu
Keyword(s):  
Super Resolution ◽  
Infrared Imagery ◽  
Generative Adversarial Network ◽  
Adversarial Network
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