罗密特·莫利克、希曼舒·夏尔马、索米尔·帕特尔、贝萨尼·卢施、伊莉丝·詹宁斯
我们概述了 OpenFOAM 中数据科学模块的开发，该模块允许在场内部署经过培训的深度学习体系结构，以执行通用预测任务。此模块由 TensorFlow C API 构建，并集成为 OpenFOAM，作为可能在运行时链接的应用程序。值得注意的是，我们的公式排除了与神经网络架构类型（即卷积、完全连接等）相关的任何限制。这允许对复杂的神经结构进行潜在的研究，解决实际的CFD问题。此外，拟议的模块概述了建立计算流体动力学和机器学习的开源、统一和透明的框架的道路。
Deploying deep learning in OpenFOAM with TensorFlow
Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings
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.
