@inproceedings{5f155059a39e45baa69d546043f2a1f0,
title = "Data-driven Sensor Placement for Fluid Flows",
abstract = "Optimal sensor placement for fluid flows is an important and challenging problem. We propose a novel method for sensor placement that leverages recent advancements in data-driven reduced-order modeling techniques. The proposed methodology can be used in conjunction with any data-driven modeling technique that provides a linear approximation of the fluid dynamics. We use adjoint-based gradient descent to find sensor locations that minimize the trace of an approximation of the estimation error covariance matrix. We also propose an augmented objective function can is more suited for control-oriented applications. We demonstrate the performance of our method for reconstruction and prediction of the complex linearized Ginzburg-Landau equation in the globally unstable regime.",
author = "Palash Sashittal and Bodony, {Daniel J.}",
note = "Funding Information: This work was sponsored, in part, by the Office of Naval Research (ONR) as part of the Multidisciplinary University Research Initiatives (MURI) Program, under grant number N00014-16-1-2617. Publisher Copyright: {\textcopyright} 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021 ; Conference date: 02-08-2021 Through 06-08-2021",
year = "2021",
doi = "10.2514/6.2021-2824",
language = "English (US)",
isbn = "9781624106101",
series = "AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021",
}