## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Parameter Criticality Assessment
#### 3.2.2 Design of Experiments  
The systematic method of designing and evaluating controlled experiments to assess critical variables in optimization procedure is known as design of experiments. This technique improves efficiency by eliminating non-critical parameters and accuracy by allowing designers to gather as much information as possible from fewer experimental runs [14; 50]. The DoE's key techniques include the following: the Taguchi method [58], which emphasizes robust design by minimizing variation; fractional factorial designs, which minimize the number of runs while maintaining crucial information; and full factorial designs, which examine every possible combination of factors. Another method, Central Composite Design (CCD), constructs a response surface in order to fit a second order polynomial model and capture non-linearities between factors and responses. Alongside these, Latin Hypercube Sampling (LHS) and Monte Carlo analysis are widely used to sample points to construct surrogate models and account for non-linearities. DoE is widely used in the electronics, automotive, and aerospace manufacturing sectors to optimize the design parameters for increased efficiency and reliability [59; 60]. In [14], as previously discussed, successfully implements the DoE methodology, resulting in a notable reduction in the overall computational time of the optimization process. In [61], the authors discuss the design of a 12/10 hybrid excitation segmented-rotor SRM (HESRSM), where magnets are placed in front of the openings in the stator slots. The paper selects the Taguchi optimization method for its exceptional efficiency and practicality in handling multi-objective motor design problems. By replacing a full factorial design (15,625 trials) with just 25 simulation runs using an orthogonal array, the approach achievesa 99.84% reduction in computational effort. [PERSON]'s framework also supports multi-objective optimization, enabling the authors to balance improvements in average torque, torque ripple, and torque density. Alongside the Taguchi method, a One-at-a-Time (OAT) sensitivity analysis is employed to limit the search space and optimize the parameters. The optimization focuses on reducing torque ripple, improving average torque, and minimizing losses using equations developed for the geometric characteristics, while the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles are indirectly calculated using these equations. Thus, this process can be considered an indirect system-level optimization. However, this approach does not account for the non-linear characteristics of the system, which may affect the accuracy of the optimization results. For the static analysis of the single phase, ALTAIR FLUX software is used to extract the torque profile and the coupled flux, and ALTAIR FLUX is coupled with MATLAB/Simulink for dynamic operation of the motor. The optimization results indicate a significant increase in torque and power density, with only a slight reduction in ripple. Reference [62] addresses system-level optimization using a sensitivity analysis from the perspective of robustness. The authors propose a method that incorporates the driving cycle and multiple driving modes into the analysis. This method utilizes variance-based sensitivity analysis to eliminate non-critical parameters, combined with the Taguchi method for optimization. A case study of a 12/10 six-phase SRM is conducted to validate the method, where each design parameter, whether of the controller or the motor, affects different driving modes. The analysis is completed after four iterations, and the results show a significant reduction in torque ripple with a slight decrease in torque. To improve real-world feasibility, the study adopts a robust design strategy by modeling manufacturing tolerances, such as air gap variation, as noise factors and optimizing based on signal-to-noise ratios, ensuring the final design performs reliably under production variability.