Computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks

It is expensive to compute residual diffusivity in chaotic in-compressible flows by solving advection-diffusion equation due to the formation of sharp internal layers in the advection dominated regime. Proper orthogonal decomposition (POD) is a classical method to construct a small number of adaptive orthogonal basis vectors for low cost computation based on snapshots of fully resolved solutions at a particular molecular diffusivity \(D_{0}^{*}\). The quality of POD basis deteriorates if it is applied to \(D_0\ll D_{0}^{*}\). To improve POD, we adapt a super-resolution generative adversarial deep neural network (SRGAN) to train a nonlinear mapping based on snapshot data at two values of \(D_{0}^{*}\). The mapping models the sharpening effect on internal layers as \(D_0\) becomes smaller. We show through numerical experiments that after applying such a mapping to snapshots, the prediction accuracy of residual diffusivity improves considerably that of the standard POD.