## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Sequential Subspace Optimization Method
This gap likely stems from the high computational cost and complexity associated with such an analysis due to the strong coupling between the machine and the controller, thereby necessitating a multiphysics-based approach for a more comprehensive optimization process. Furthermore, most of the reviewed studies either overlook transient electromagnetic and thermal effects or, in cases that involve dynamic-state simulations, do not explicitly consider them. This is mainly due to the added complexity and computational effort required to include such detailed simulations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Modeling** & **Optimization** & **System-Level** & \begin{tabular}{c} **Optimization** \\ **Algorithm** \\ \end{tabular} & \
\begin{tabular}{c} **Optimization** \\ **Parameters** \\ **Machine/Control** \\ \end{tabular} &
\begin{tabular}{c} **Optimization** \\ **Goals** \\ \end{tabular} & **Application** \\ \hline
[50] & \begin{tabular}{c} RBFNN with \\ SA \\ \end{tabular} & \begin{tabular}{c} GA, PSO, \\ GRSM \\ \end{tabular} & Multi-Level & \
\begin{tabular}{c} Candidate \\ Models/ \\ \(\theta_{on},\theta_{off}\) \\ \end{tabular} &
\begin{tabular}{c} Radial Force \\ Harmonics, Torque \\ Ripple, Average \\ Torque \\ \end{tabular} & \multirow{4}{*}{SRM Drives} \\ \hline
[54] & \begin{tabular}{c} Analytical \\ Equations with \\ SA \\ \end{tabular} & DE & Single-Level & \
\begin{tabular}{c} Geometrical \\ Parameters/ \\ \(\theta_{on},\theta_{off}\) \\ \end{tabular} &
\begin{tabular}{c} Average Torque, \\ Current Density, \\ Torque Ripple \\ \end{tabular} & \multirow{4}{*}{SRM Drives} \\ \hline
[55] & \
\begin{tabular}{c} Voltage-fed \\ Analytical \\ Equations and \\ FEM \\ \end{tabular} & PSO & & & &
\begin{tabular}{c} Average Torque, \\ Torque Ripple, \\ Efficiency \\ \end{tabular} & \multirow{4}{*}{SRM Drives} \\ \hline
[56;57] & FEM with SA & \begin{tabular}{c} [56] \\ CD-NSGA-II \\ \end{tabular} & Single-Level & \
\begin{tabular}{c} Geometrical \\ Parameters/ \\ \(\theta_{on},\theta_{off}\) \\ \end{tabular} &
\begin{tabular}{c} [56] Efficiency, \\ Average Torque, \\ \([56,57]\) Torque \\ Ripple \\ \end{tabular} & \multirow{4}{*}{E-Bikes} \\ \hline
[61;62] & \
\begin{tabular}{c} Taguchi with \\ SA \\ \end{tabular} & Taguchi & & & &
\begin{tabular}{c} Average Torque, \\ Torque Ripple, \\ Torque Density \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._To address these computational challenges and facilitate the integration of emerging methodologies, we propose a structured roadmap aimed at overcoming key limitations in current SLDOM practices. First, by incorporating ML-based algorithms into the modeling of the machine, its electromagnetic, thermal, and mechanical characteristics with minimal loss in fidelity are shown to be captured, thereby enabling offline training. Model complexity is to be controlled through dimensionality reduction techniques, such as sensitivity analysis and DoE. Second, the integration of Model Predictive Control (MPC) is particularly advantageous due to its predictive nature and its ability to directly incorporate dynamic controller performance metrics, such as overshoot and settling time. MPC can continuously update control actions based on predicted system trajectories, making it well-suited for coordinated machine-controller optimization. Accordingly, the proposed roadmap follows the following sequence: offline generation and validation of ML-based surrogate models; integration of these models into an MPC-based co-optimization framework.