Deploying deep learning in OpenFOAM with TensorFlow
This work provides an open-source, unified framework for computational fluid dynamics and machine learning, which is significant for researchers and practitioners in CFD who want to integrate deep learning models directly into their simulations.
This paper describes a new module for OpenFOAM that integrates TensorFlow's C API, enabling the in-situ deployment of various deep learning architectures for predictive tasks within computational fluid dynamics simulations. This integration allows for the use of any neural network type, opening possibilities for complex architecture studies in practical CFD problems.
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.