  Information loss in numerical physics simulations can arise from various
sources when solving discretized partial differential equations. In particular,
errors related to numerical precision ("sub-precision errors") can accumulate
in the quantities of interest when the simulations are performed using
low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit
simulation. On the other hand, low-precision computation is less resource
intensive than high-precision computation. Several machine learning techniques
proposed recently have been successful in correcting errors due to coarse
spatial discretization. In this work, we extend these techniques to improve CFD
simulations performed with low numerical precision. We quantify the
precision-related errors accumulated in a Kolmogorov forced turbulence test
case. Subsequently, we employ a Convolutional Neural Network together with a
fully differentiable numerical solver performing 16-bit arithmetic to learn a
tightly-coupled ML-CFD hybrid solver. Compared to the 16-bit solver, we
demonstrate the efficacy of the hybrid solver towards improving various metrics
pertaining to the statistical and pointwise accuracy of the simulation.
