# Forward Gradients for Data-Driven CFD Wall Modeling

arXiv: https://arxiv.org/abs/2311.11876 · HTML: https://mlsanity.com/paper/2311.11876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Gradients without Backpropagation](https://mlsanity.com/paper/2202.08587/) (2022)
- [Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks](https://mlsanity.com/paper/1912.12969/) (2019)

## Abstract (arXiv)

Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications. However, the practical use is often limited by the high computational cost, and the accurate resolution of near-wall flow is a significant contributor to this cost. Machine learning (ML) and other data-driven methods can complement existing wall models. Nevertheless, training these models is bottlenecked by the large computational effort and memory footprint demanded by back-propagation. Recent work has presented alternatives for computing gradients of neural networks where a separate forward and backward sweep is not needed and storage of intermediate results between sweeps is not required because an unbiased estimator for the gradient is computed in a single forward sweep. In this paper, we discuss the application of this approach for training a subgrid wall model that could potentially be used as a surrogate in wall-bounded flow CFD simulations to reduce the computational overhead while preserving predictive accuracy.