This study presents a cluster-based Bayesian methodology for state estimation under realistic conditions including noisy data from sparse sensors. The proposed approach is …
Inspired by biological swimming and flying with distributed sensing, we propose a data- driven approach for load estimation that relies on complex networks. We exploit sparse, real …
This study proposes a data-driven methodology to complement existing time-series measurement tools for turbulent flows. Specifically, a cluster-based transition network model …