## 1 Introduction
Here, each local ROM is post-processed via semidefinite programming to enforce strict dissipativity (passivity) so that it is stable. By interpolating these modified (dissipative) reduced models, the resulting parametric ROM remains stable for any intermediate parameter value. A data-driven Loewner framework for aeroelastic systems is introduced in [9]. The authors explore data-driven identification  
Figure 1: Basic process of pMOR. for parametric ROMs of aeroelastic systems. It uses a technique for modeling unsteady aerodynamic forces with parameter dependence. The approach employs the Loewner framework to fit parametric state-space models to frequency-domain data at multiple parameter settings, effectively interpolating transfer-function data into a reduced-order model. Optimization-based \(\mathcal{H}_{2}\otimes\mathcal{L}_{2}\) reduction was advanced in [10]. The paper demonstrated that the \(\mathcal{H}_{2}\otimes\mathcal{L}_{2}\) reduction framework extends to parametric LTI systems: first-order optimality conditions for structured ROMs were derived, and a stability-preserving optimization routine was devised, which yields locally optimal, stable reduced models with markedly lower errors at modest computational cost. Port-Hamiltonian structure preservation for pMOR was addressed by [11]. The study contributes a method aimed at preserving the inherent structure (e.g., port-Hamiltonian) of the original system in the reduced models. The approach integrates structure-preserving constraints into the MOR process so that the parametric ROM inherits properties like stability and passivity from the full-order model. Passivity-preserving reduction across parameter ranges was discussed in [12]. The authors focus on preserving physical system structure during parametric reduction. It introduces a pMOR technique that ensures the ROM remains passive (energy non-generating and stable) for all parameter values. Another crucial aspect is ensuring that the interpolated model behaves similarly to the original high-order system. Since interpolation relies on information from a given set of reduced models, the parameter domain must be sampled densely enough to capture the system's behavior accurately. More specifically, the combination of known models and a priori knowledge of the original system provides sufficient information to construct an interpolated model that closely resembles the original system from an application-specific perspective. In most parametric LTI model interpolation, system evaluations are performed at selected parameter values, which serve as anchor points for interpolation. However, selecting these values is a non-trivial task. Poor choices can lead to significant errors, especially in regions with rapid system variation. To improve accuracy, the samples should capture key changes in system behavior. Rather than distributing the points uniformly, methods like adaptive sampling and sensitivity analysis are used to place the samples where they are most informative [13; 14]. These techniques propose an automated adaptive sampling strategy for parametric model order reduction (pMOR) using matrix interpolation, where the sampling points are chosen based on subspace distances in order to efficiently generate reduced models. The downside of these methods is that it is not evident how subspace distances translate to system behavior. In this regard, the paper presents a novel sampling criterion that links the criteria to the pole locations that can be interpreted easily by a trained user as system behavior. An effective application of such algorithms is in optimizing drone structures. Enhancing unmanned aerial vehicle (UAV) design to minimize vibration improves flight stability, sensor accuracy, and component lifespan. In fixed-wing UAVs, it also helps mitigate flutter [15]. Drones are widely used in aerial surveying [16] and agriculture [17], but structural advancements can further enhance efficiency and durability.