  Multi-scale, multi-fidelity numerical simulations form the pillar of
scientific applications related to numerically modeling fluids. However,
simulating the fluid behavior characterized by the non-linear Navier Stokes
equations are often times computational expensive. Physics informed machine
learning methods is a viable alternative and as such has seen great interest in
the community [refer to Kutz (2017); Brunton et al. (2020); Duraisamy et al.
(2019) for a detailed review on this topic]. For full physics emulators, the
cost of network inference is often trivial. However, in the current paradigm of
data-driven fluid mechanics models are built as surrogates for complex
sub-processes. These models are then used in conjunction to the Navier Stokes
solvers, which makes ML model inference an important factor in the terms of
algorithmic latency. With the ever growing size of networks, and often times
overparameterization, exploring effective network compression techniques
becomes not only relevant but critical for engineering systems design. In this
study, we explore the applicability of pruning and quantization (FP32 to int8)
methods for one such application relevant to modeling fluid turbulence.
Post-compression, we demonstrate the improvement in the accuracy of network
predictions and build intuition in the process by comparing the compressed to
the original network state.
