## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Sequential Subspace Optimization Method
The results are validated by comparing the FEM simulations with experimental data from a prototype SSRM drive system. Torque measurements at 6000 rpm align closely with simulation predictions. Finally, this paper serves as a preliminary study for [38], by the same authors, which refines the approach by introducing multi-level optimization, better Kriging model validation, and a more adaptable method for selecting the near-optimal design based on specific application needs. Having reviewed the studies focused on techniques for computational time and model complexity reduction, Table 1 presents the quantitative impact of these methods on simulation time, number of simulations, and model accuracy, benchmarked against the FEM. A substantial reduction in computational burden is observed, while model accuracy is compromised minimally. Among the surrogate modeling techniques, the Kriging model emerges as the most commonly adopted, owing to its ability to significantly reduce computational effort with only minor deviations from the true model. It is also noted that for studies lacking explicit numerical data, estimations have been conducted based on the reported outcomes and discussions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Kriging Model** & **RSM** & **ANN** & **Models** & **Models** & **Models** \\  & & & & **Employing SA** & **Employing DoE** & **Employing SSOM** \\ \hline  & [30] from 30 min to a few seconds & & & [54] 33\% fewer parameters in simulations & [61] from 15,625 simulations (-99.84\% reduction) & \\ \multirow{4}{*}{
\begin{tabular}{c} **Computational** \\ **time/number** \\ **of simulations** \\ **FEM** \\ \end{tabular} } & [36] from 100 thousand to 1125 simulations (-99\% reduction) & 559 simulations from 3000 ones & [50] from (80\% reduction), & [56] from 33 h to 153 h to roughly 6 (-5 times faster) & [61] from 15,625 simulations (-99.84\% reduction) & \\ \cline{2-5}  & [37] from 1331 to 726 simulations (-45\% reduction) & & [56] from 33 h to roughly 6 (-5 times faster) & [62] from 81 to 18 simulations (78\% reduction) & \\ \cline{2-5}  & [38] from 27 million to 1331 simulations (-99.995\% reduction) & & & [54] max 23\% overprediction & [61] maximum error 1\% & [66] errors well within 1\% in the convergence \\ \cline{2-5}  & [36] from 27 million to 1331 simulations (-99.995\% reduction) & & & [56] maximum error 5\% & [62] maximum error 5\% \\ \hline  & [30] 1-2\% discrepancies & & & [54] max 23\% overprediction & [61] maximum error 1\% & \\ \cline{2-5}  & [36] decent accuracy & & [14] almost 100\% accuracy & [50] average error 1.5\%, maximum error & [55] maximum error 1.5\%, maximum error 2\% & \\ \cline{2-5}  & [37] 1.79\% maximum error & & & 3\% & \\ \cline{2-5}  & [38] average error 1\%, maximum error 3.1\% & & & [54] max 23\% overprediction & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of surrogate modeling techniques in terms of computational efficiency and accuracy relative to the FEM. Table 2 provides a summary of the research papers that have been classified under the category of SLDOM and discussed in detail in the previous sections. It outlines the specific content of each study, along with the techniques employed to achieve SLDOM. Regarding the \"Optimization Parameters\" section, the term \"Geometrical Parameter\" refers to various geometric features of the motor. Due to their abundance and in order to maintain the table's clarity, these parameters have been grouped under a single category rather than listed individually. Moreover, the studies referring to SRM/SRG drives employ a general terminology, allowing their findings and methodologies to be applicable across a wide range of industrial applications.