2021
DOI: 10.1007/s00162-021-00580-0
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Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

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Cited by 61 publications
(24 citation statements)
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“…One of the issues associated with the MLP is that the number of edges inside it may explode due to its fully-connected structure when handling high-dimensional data such as fluid flows. To overcome this issue in dealing with fluid flow problems, a convolutional neural network (CNN) 19 has widely been accepted as a good candidate 3 , 20 . We capitalize on the combination of two- and three-dimensional CNNs for the state estimation task in the present study.…”
Section: Regression Methodsmentioning
confidence: 99%
“…One of the issues associated with the MLP is that the number of edges inside it may explode due to its fully-connected structure when handling high-dimensional data such as fluid flows. To overcome this issue in dealing with fluid flow problems, a convolutional neural network (CNN) 19 has widely been accepted as a good candidate 3 , 20 . We capitalize on the combination of two- and three-dimensional CNNs for the state estimation task in the present study.…”
Section: Regression Methodsmentioning
confidence: 99%
“…The present CNN is composed of convolutional layers, which allows us to extract spatial coherent features of data through filter operations. Note that pooling or upsampling operations are not considered in the present study because no dimension reduction or expansion is required for the present model such that R 𝑑 input = R 𝑑 output , where 𝑑 input and 𝑑 output denote the vector dimensions of input and output data, respectively (Morimoto et al, 2021c). The operation at the 𝑠-th convolutional layer 𝑞 (𝑠) can mathematically be expressed as…”
Section: Convolutional Neural Network-based State Estimator For Fluid...mentioning
confidence: 99%
“…SWAG relies on estimating weight distributions for arbitrary neural-network architectures. To demonstrate this, we consider two types of architectures: (i) MLP-CNN-based estimator [74,75,76] and (ii) CNN [14,18,77,78]. These two examples have respectively been used for a broad range of fluid-flow approximations.…”
Section: Machine-learning Modelsmentioning
confidence: 99%