  We outline the development of a data science module within OpenFOAM which
allows for the in-situ deployment of trained deep learning architectures for
general-purpose predictive tasks. This module is constructed with the
TensorFlow C API and is integrated into OpenFOAM as an application that may be
linked at run time. Notably, our formulation precludes any restrictions related
to the type of neural network architecture (i.e., convolutional,
fully-connected, etc.). This allows for potential studies of complicated neural
architectures for practical CFD problems. In addition, the proposed module
outlines a path towards an open-source, unified and transparent framework for
computational fluid dynamics and machine learning.
