## 4 Proposed Solution-Machine Learning Modeling Coupled with Model Predictive Control  
ML-based modeling has already shown promising results, primarily in reducing computational time, while its accuracy can be further improved by using larger models and more training data. Naturally, these two aspects are contradictory, making it essential to find a balance between them. Nevertheless, the excessive computational burden required by the FEM can be significantly reduced by replacing it with ML models. Most studies focus on supervised learning, while reinforcement learning has not yet been widely applied to motor design but has shown potential in control strategies [69]. Multiphysics analysis of an SRM is crucial for determining the final design, but it is also highly complex and time consuming [70]. In most cases, the electromagnetic analysis is performed using the FEM, while the thermal aspect is either neglected or a coupled electromagnetic-thermal analysis is conducted, and the mechanical aspect is entirely omitted [71]. The latter approach remains relatively time-consuming and does not account for the mechanical analysis, which determines the radial forces, vibration, and acoustic noise, which are in turn further influenced by the power converter and its switching frequency [72]. One common approach adopted by several of the aforementioned studies is the incorporation of thermal and mechanical factors as constraints in the optimization process, either simultaneously, as in [24; 25; 27; 37; 50], or sequentially, as in [14; 40; 55], following the electromagnetic analysis. This is typically achieved by considering parameters such as current density, iron and copper losses, and torque-current density, which indirectly assist in regulating temperature rise and torque ripple for radial forces reduction. While this method effectively mitigates the high computational cost associated with full thermal and mechanical analyses, it does not fully capture the actual operational behavior of the motor. As such, a primary suggestion is to evaluate candidate topologies using the constraints, and perform thermal and mechanical analysis with the promising designs. The integration of ML algorithms in the design process aims to reduce the implementation time of these analyses and to map a relationship between the three domains. However, one downside is that numerous FEM simulations are required to train the model. Nevertheless, the number of simulations needed is significantly lower compared to those required for a full simulation involving all three types of analysis. Along with the FEM, Computational Fluid Dynamics (CFD) is specifically used for the three analyses [73; 74]. By employing ML algorithms, their use is limited to training and evaluating the model through simulations, as well as to the final validation of the optimization process. The authors of [71] describe a method that combines electromagnetic/thermal analysis with an ANN to accelerate the optimization of a Surface Permanent Magnet machine. The ANN is used to determine the maximum current density for each design generated during the optimization process. Training of the ANN is performed using data from the first 10 generations of simulations, representing 70% of the simulations, with the remaining 15% used for validation and 15% for testing. The results show improvements compared to using the FEM alone, with the simulation time reduced by up to three times. This analysis demonstrates that the proposed approach yields positive outcomes and can also be applied to SRM, particularly if controller parameters such as switching frequency are considered for mechanical analysis. This would enable a comprehensive SLDOM analysis. The most prominent control strategies proposed for SRM drives are Direct Torque Control (DTC), Field-Oriented Control (FOC), Current Chopping Control (CCC), Angle Position Control (APC), and MPC [75]. CCC is the most widely used due to its simplicity, low complexity, and minimal computational burden.