A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. (arXiv:2201.03617v1 [physics.flu-dyn])

Analyzing large-scale data from simulations of turbulent flows is memory
intensive, requiring significant resources. This major challenge highlights the
need for data compression techniques. In this study, we apply a
physics-informed Deep Learning technique based on vector quantization to
generate a discrete, low-dimensional representation of data from simulations of
three-dimensional turbulent flows. The deep learning framework is composed of
convolutional layers and incorporates physical constraints on the flow, such as
preserving incompressibility and global statistical characteristics of the
velocity gradients. …

arxiv autoencoder compression data physics