Our paper on Reynolds-averaged Navier Stokes simulations with deep learning (i.e. convolutional neural networks) is online now. You can read it on arXiv:
https://arxiv.org/abs/1810.08217
The full source code and a first training data set with 6400 RANS simulations as ground truth data is available here:
https://github.com/thunil/Deep-Flow-Prediction
Deep Flow Prediction is a pytorch framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning. It contains code for data generation, network training, and evaluation for the aforementioned paper. A brief manual for the ode can be found in the github repository.
Paper Details:
Title: Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations
Authors: Nils Thuerey, Konstantin Weissenow, Harshit Mehrotra, Nischal Mainali, Lukas Prantl, Xiangyu Hu
Abstract: With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.
