Many engineering problems require finding the best design or control strategy for systems that are expensive to simulate. We develop adjoint-based and Bayesian optimisation methods, reduced-order models that capture the essential dynamics at a fraction of the cost, and machine-learning tools for sensing and monitoring.

Topics

• Adjoint-based and parallel-in-time optimisation of unsteady flows
• Bayesian optimisation for flow control
• Nonlinear model reduction
• Neural networks for sensor monitoring

People

Miguel Ángel Fosas de Pando, Luis Lafuente Molinero

Selected publications

• S. Costanzo, T. Sayadi, M. Fosas de Pando, P. J. Schmid, P. Frey (2022). Parallel-in-time adjoint-based optimization – application to unsteady incompressible flows. Journal of Computational Physics. doi:10.1016/j.jcp.2022.111664
• A. Larroque, M. Fosas de Pando, L. Lafuente (2022). Cylinder drag minimization through wall actuation: A Bayesian optimization approach. Computers & Fluids. doi:10.1016/j.compfluid.2022.105370
• L. J. Muñoz-Molina, I. Cazorla-Piñar, J. P. Domínguez-Morales, L. Lafuente, F. Pérez-Peña (2022). Real-time detection of uncalibrated sensors using neural networks. Neural Computing and Applications. doi:10.1007/s00521-021-06865-z
• D. Sipp, M. Fosas de Pando, P. J. Schmid (2020). Nonlinear model reduction: A comparison between POD-Galerkin and POD-DEIM methods. Computers & Fluids. doi:10.1016/j.compfluid.2020.104628