  Reliable prediction of turbulent flows is an important necessity across
different fields of science and engineering. In Computational Fluid Dynamics
(CFD) simulations, the most common type of models are eddy viscosity models
that are computationally inexpensive but introduce a high degree of epistemic
error. The Eigenspace Perturbation Method (EPM) attempts to quantify this
predictive uncertainty via physics based perturbation in the spectral
representation of the predictions. While the EPM outlines how to perturb, it
does not address how much or even where to perturb. We address this need by
introducing machine learning models to predict the details of the perturbation,
thus creating a physics inspired machine learning (ML) based perturbation
framework. In our choice of ML models, we focus on incorporating physics based
inductive biases while retaining computational economy. Specifically we use a
Convolutional Neural Network (CNN) to learn the correction between turbulence
model predictions and true results. This physics inspired machine learning
based perturbation approach is able to modulate the intensity and location of
perturbations and leads to improved estimates of turbulence model errors.
