no code implementations • 21 Dec 2023 • Josh Dees, Antoine Jacquier, Sylvain Laizet
On the theoretical side, we develop a complexity analysis of this approach, and show numerically that random quantum networks can outperform more traditional quantum networks as well as random classical networks.
no code implementations • 3 Feb 2023 • Ali Girayhan Özbay, Sylvain Laizet
In many practical fluid dynamics experiments, measuring variables such as velocity and pressure is possible only at a limited number of sensor locations, \textcolor{black}{for a few two-dimensional planes, or for a small 3D domain in the flow}.
1 code implementation • 19 Jul 2022 • Mike Diessner, Joseph O'Connor, Andrew Wynn, Sylvain Laizet, Yu Guan, Kevin Wilson, Richard D. Whalley
To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems.
1 code implementation • 8 Feb 2022 • Ali Girayhan Özbay, Sylvain Laizet
Different NNs for different sensor setups (where information about the flow is collected) are trained with high-fidelity simulation data for a Reynolds number equal to approximately $300$ for 64 objects randomly generated using Bezier curves.
1 code implementation • 18 Oct 2019 • Ali Girayhan Özbay, Arash Hamzehloo, Sylvain Laizet, Panagiotis Tzirakis, Georgios Rizos, Björn Schuller
The Poisson equation is commonly encountered in engineering, for instance in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field.
1 code implementation • 3 Oct 2019 • Heng Xiao, Jin-Long Wu, Sylvain Laizet, Lian Duan
However, a major obstacle in the development of data-driven turbulence models is the lack of training data.
Fluid Dynamics