(Created page with " == Abstract == We develop a wavelet-based three-dimensional convolutional neural network (WCNN3d) for superresolution of coarse-grained data of homogeneous isotropic turbule...") |
m (Scipediacontent moved page Draft Content 577186525 to Asaka et al 2022a) |
(No difference)
| |
Revision as of 15:51, 6 July 2022
Abstract
We develop a wavelet-based three-dimensional convolutional neural network (WCNN3d) for superresolution of coarse-grained data of homogeneous isotropic turbulence. The turbulent flow data are computed by high resolution direct numerical simulation (DNS), while the coarse-grained data are obtained by applying a Gaussian filter to the DNS data. The CNNs are trained with the DNS data and the coarse-grained data. We compare vorticityand velocity-based approaches and assess the proposed
Document
Back to Top
GET PDF
Document information
Published on 05/07/22
Submitted on 05/07/22
Volume 1700 Data Science, Machine Learning and Artificial Intelligence, 2022
DOI: 10.23967/wccm-apcom.2022.013
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?