Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps
  in Reacting PIV Data
Shashank Yellapantula
In this study, a deep learning based conditional density estimation technique
known as conditional variational auto-encoder (CVAE) is used to fill gaps
typically observed in particle image velocimetry (PIV) measurements in
combustion systems. The proposed CVAE technique is trained using time resolved
gappy PIV fields, typically observed in industrially relevant combustors.
Stereo-PIV (SPIV) data from a swirl combustor with very a high vector yield is
used to showcase the accuracy of the proposed CVAE technique. Various error
metrics evaluated on the reconstructed velocity field in the gaps are presented
from data sets corresponding to three sets of combustor operating conditions.
In addition to accurate data reproduction, the proposed CVAE technique offers
data compression by reducing the latent space dimension, enabling the efficient
processing of large-scale PIV data.