Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics. (arXiv:2211.00307v1 [physics.flu-dyn])

There is a critical need for efficient and reliable active flow control
strategies to reduce drag and noise in aerospace and marine engineering
applications. While traditional full-order models based on the Navier-Stokes
equations are not feasible, advanced model reduction techniques can be
inefficient for active control tasks, especially with strong non-linearity and
convection-dominated phenomena. Using convolutional recurrent autoencoder
network architectures, deep learning-based reduced-order models have been
recently shown to be effective while performing several orders of magnitude
faster than full-order …

arxiv convolution dynamics fluid dynamics physics space