  Recent applications of machine learning, in particular deep learning,
motivate the need to address the generalizability of the statistical inference
approaches in physical sciences. In this letter, we introduce a modular physics
guided machine learning framework to improve the accuracy of such data-driven
predictive engines. The chief idea in our approach is to augment the knowledge
of the simplified theories with the underlying learning process. To emphasise
on their physical importance, our architecture consists of adding certain
features at intermediate layers rather than in the input layer. To demonstrate
our approach, we select a canonical airfoil aerodynamic problem with the
enhancement of the potential flow theory. We include features obtained by a
panel method that can be computed efficiently for an unseen configuration in
our training procedure. By addressing the generalizability concerns, our
results suggest that the proposed feature enhancement approach can be
effectively used in many scientific machine learning applications, especially
for the systems where we can use a theoretical, empirical, or simplified model
to guide the learning module.
