  Deep Learning (DL) algorithms are emerging as a key alternative to
computationally expensive CFD simulations. However, state-of-the-art DL
approaches require large and high-resolution training data to learn accurate
models. The size and availability of such datasets are a major limitation for
the development of next-generation data-driven surrogate models for turbulent
flows. This paper introduces SURFNet, a transfer learning-based
super-resolution flow network. SURFNet primarily trains the DL model on
low-resolution datasets and transfer learns the model on a handful of
high-resolution flow problems - accelerating the traditional numerical solver
independent of the input size. We propose two approaches to transfer learning
for the task of super-resolution, namely one-shot and incremental learning.
Both approaches entail transfer learning on only one geometry to account for
fine-grid flow fields requiring 15x less training data on high-resolution
inputs compared to the tiny resolution (64x256) of the coarse model,
significantly reducing the time for both data collection and training. We
empirically evaluate SURFNet's performance by solving the Navier-Stokes
equations in the turbulent regime on input resolutions up to 256x larger than
the coarse model. On four test geometries and eight flow configurations unseen
during training, we observe a consistent 2-2.1x speedup over the OpenFOAM
physics solver independent of the test geometry and the resolution size (up to
2048x2048), demonstrating both resolution-invariance and generalization
capabilities. Our approach addresses the challenge of reconstructing
high-resolution solutions from coarse grid models trained using low-resolution
inputs (super-resolution) without loss of accuracy and requiring limited
computational resources.
