Tall buildings exposed to wind experience wind-induced loads that create pressure on the building envelope, and their integral effects cause the structure to move in the dominant directions, namely along-wind, across-wind, and torsional [1, 2, 3]. The description of the pressure field around a building does not lend itself to a simple functional relationship with approach flow turbulence. As a result, calls for reliance on wind tunnel experiments have been made. These tests rely heavily on pressure taps connected to pressure sensors to monitor pressure fields over the building surface. A basic question is where to deploy available sensors to accurately predict and reconstruct the structure of a wind pressure field from limited and noisy sensor outputs. In fact, the optimal sensor placement problem has garnered considerable attention for a long time, as fast data acquisition, analysis, and decision in high-performance control for complex systems can be archived with a small number of measurements at limited locations. In practice, the best locations for sensors in regular structures with simple geometry and a small number of degrees of freedom can be determined empirically using engineering judgment and past experience. However, for a complicated large-scale structure, a systematic and efficient approach is required because the solution space is far beyond the capabilities of hand calculation [4, 5, 6, 7, 8]. Mathematically, the goal is to find m positions from a set of n positions that maximize the information about the behaviors of a structural system: n! c =

Although both the public sector and defense agencies are interested in urban uncrewed aerial vehicle (UAV) mission performance, fixed winged aircraft are still incapable of adapting to the complex aerodynamics within a city environment [1, 2, 3, 4, 5, 6]. Currently, the most dynamic environments are dominated by multirotor flight vehicles; however, the highly maneuverable and responsive quadrotor design suffers from substantial weight and power constraints, limiting the operational range and on-board computational capabilities needed for autonomy [7, 8, 9, 10]. Current fixed wing UAVs have greater range but are not as maneuverable [11]. Counter to both rotorcraft and traditional fixed wing UAV design, birds can adapt their wing shape as the environment changes to achieve both efficient and maneuverable flight [12]. This ability supports birds of prey in navigating through complex environments [13], or rejecting perturbations in a gusty environment [14, 15].