FlowDNN: a physics-informed deep neural network for fast and accurate flow prediction

In this paper, we propose FlowDNN, a novel deep neural network (DNN) to efficiently learn flow representations from CFD results. FlowDNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes. FlowDNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction. This approach not only improves the prediction accuracy but also preserves the physical consistency of the predicted flow fields, which is essential for CFD. Download paper here

Recommended citation: Donglin Chen, Xiang Gao, Chuanfu Xu, Siqi Wang, Shizhao Chen, Jianbin Fang, Zheng Wang. (2021). “FlowDNN: a physics-informed deep neural network for fast and accurate flow prediction.” FITEE. 2021.