A wavelet-based three-dimensional Convolutional Neural Network for superresolution of turbulent vorticity


== 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


<pdf>Media:Draft_Content_577186525-2785_paper-4258-document.pdf</pdf>

== Document ==
<pdf>Media:Draft_Content_577186525-2785_paper-1211-document.pdf</pdf>