From the 2 of 27 linked papers with an AI index.
13 papers · 1 filter
Latent-Space Non-Linear Model Predictive Control for Partially-Observable Systems
Luigi Marra, Onofrio Semeraro, Lionel Mathelin +2
This work presents a scalable control framework based on nonlinear Model Predictive Control for high-dimensional dynamical systems. The proposed approach addresses the key challeng…
A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
We present a data-driven active flow control framework designed for deployment with few non-intrusive sensors. The method builds upon Artificial Intelligence driven reduced-order p…
Sensor optimization for urban wind estimation with cluster-based probabilistic framework
Yutong Liang, Chang Hou, Guy Y. Cornejo Maceda +8
We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulation…
Model-based time super-sampling of turbulent flow field sequences
Qihong Lorena Li-Hu, Patricia GarcÃa-Caspueñas, Andrea Ianiro +1
We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of…
Full-domain POD modes from PIV asynchronous patches
Iacopo Tirelli, Adrian Grille Guerra, Andrea Ianiro +3
A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (ove…
An efficient offline sensor placement method for flow estimation
Junwei Chen, Marco Raiola, Stefano Discetti
We present an efficient method to optimize sensor placement for flow estimation using sensors with time-delay embedding in advection-dominated flows. Our solution allows identifyin…