# Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks

arXiv: https://arxiv.org/abs/1912.12969 · HTML: https://mlsanity.com/paper/1912.12969/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations](https://mlsanity.com/paper/2107.04556/) (2021)
- [Predicting the near-wall region of turbulence through convolutional neural networks](https://mlsanity.com/paper/2107.07340/) (2021)
- [Aim in Climate Change and City Pollution](https://mlsanity.com/paper/2112.15115/) (2021)
- [Forward Gradients for Data-Driven CFD Wall Modeling](https://mlsanity.com/paper/2311.11876/) (2023)

## Abstract (arXiv)

A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The training data are generated by performing a direct numerical simulation (DNS) at a friction Reynolds number of Reτ=180. Various networks are trained for predictions at three inner-scaled locations (y^+ = 15,~30,~50) and for different time steps between input samples Delta t+s. The inherent non-linearity of the neural-network model enables a better prediction capability than linear methods, with a lower error in both the instantaneous flow fields and turbulent statistics. Using a dataset with higher Delta t^+s improves the generalization at all the considered wall-normal locations, as long as the network capacity is sufficient to generalize over the dataset. The use of a multiple-output network, with parallel dedicated branches for two wall-normal locations, does not provide any improvement over two separated single-output networks, other than a moderate saving in training time. Training time can be effectively reduced, by a factor of 4, via a transfer learning method that initializes the network parameters using the optimized parameters of a previously-trained network.