  Over the past few years, deep learning methods have proved to be of great
interest for the computational fluid dynamics community, especially when used
as surrogate models, either for flow reconstruction, turbulence modeling, or
for the prediction of aerodynamic coefficients. Overall, exceptional levels of
accuracy have been obtained, but the robustness and reliability of the proposed
approaches remain to be explored, particularly outside of the confidence region
defined by the training dataset. In this contribution, we present a twin
auto-encoder network for incompressible laminar flow reconstruction with
uncertainty estimation around 2D obstacles. The proposed architecture is
trained over a dataset composed of numerically-computed laminar flows around
12,000 random shapes, and naturally enforces a quasi-linear relation between a
geometric reconstruction branch and the flow prediction decoder. Based on this
feature, two uncertainty estimation processes are proposed, allowing either a
binary decision (accept or reject prediction), or proposing a confidence
interval along with the flow quantities prediction (u,v,p). Results over
dataset samples as well as unseen shapes show a strong positive correlation of
this reconstruction score to the mean-squared error of the flow prediction.
Such approaches offer the possibility to warn the user of trained models when
the provided input shows too large deviation from the training data, making the
produced surrogate model conservative for fast and reliable flow prediction.
