2019
DOI: 10.48550/arxiv.1909.06042
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Sangseung Lee,
Donghyun You

Abstract: In the present study, a convolutional neural network (CNN) system which consists of multiple multi-resolution CNNs to predict future three-dimensional unsteady wake flow using flow fields in the past occasions is developed. Mechanisms of the developed CNN system for prediction of wake flow behind a circular cylinder are investigated in two flow regimes: the three-dimensional wake transition regime and the shear-layer transition regime. Understanding of mechanisms of CNNs for learning fluid dynamics is highly n… Show more

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Cited by 3 publications
(3 citation statements)
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“…In the present study, a model structure similar to a convolutional neural network (CNN) based autoencoder is used to estimate velocity field u from the input particle image q. CNNs [30] have often been utilized in the field of image recognition, and recently, use of CNNs has also been propagated in the fluid dynamics community [31][32][33][34][35][36][37][38][39].…”
Section: Machine Learning Modelmentioning
confidence: 99%
“…In the present study, a model structure similar to a convolutional neural network (CNN) based autoencoder is used to estimate velocity field u from the input particle image q. CNNs [30] have often been utilized in the field of image recognition, and recently, use of CNNs has also been propagated in the fluid dynamics community [31][32][33][34][35][36][37][38][39].…”
Section: Machine Learning Modelmentioning
confidence: 99%
“…CNNs have been performed its capability in image recognition thanks to its efficient filtering operation. Recently, the use of CNNs has also been seen in the community of fluid dynamics [49,50,51]. CNN is mainly consisted from convolutional layers and pooling layers.…”
Section: Machine Learning Schemes For Construction Of Autoencodermentioning
confidence: 99%
“…Note that the assumption on CNN is that the pixels of image far apart have no strong correlation. Although this concept was started in computer science, this has also been adopted for dealing with various high dimensional problems including fluid dynamics [33][34][35][36][37][38][39][40][41].…”
Section: Convolutional Neural Network Based Hierarchical Autoencodermentioning
confidence: 99%