Data recovery in computational fluid dynamics through deep image priors (1901.11113v2)

Abstract: One of the challenges encountered by computational simulations at exascale is the reliability of simulations in the face of hardware and software faults. These faults, expected to increase with the complexity of the computational systems, will lead to the loss of simulation data and simulation failure and are currently addressed through a checkpoint-restart paradigm. Focusing specifically on computational fluid dynamics simulations, this work proposes a method that uses a deep convolutional neural network to recover simulation data. This data recovery method (i) is agnostic to the flow configuration and geometry, (ii) does not require extensive training data, and (iii) is accurate for very different physical flows. Results indicate that the use of deep image priors for data recovery is more accurate than standard recovery techniques, such as the Gaussian process regression, also known as Kriging. Data recovery is performed for two canonical fluid flows: laminar flow around a cylinder and homogeneous isotropic turbulence. For data recovery of the laminar flow around a cylinder, results indicate similar performance between the proposed method and Gaussian process regression across a wide range of mask sizes. For homogeneous isotropic turbulence, data recovery through the deep convolutional neural network exhibits an error in relevant turbulent quantities approximately three times smaller than that for the Gaussian process regression,. Forward simulations using recovered data illustrate that the enstrophy decay is captured within 10% using the deep convolutional neural network approach. Although demonstrated specifically for data recovery of fluid flows, this technique can be used in a wide range of applications, including particle image velocimetry, visualization, and computational simulations of physical processes beyond the Navier-Stokes equations.