Deep learning architectures for optimization of electrical machine design

The optimization of electrical machine design is inherently multiobjective, involving the simultaneous consideration of torque ripple, efficiency, thermal behavior, weight, and manufacturability. Conventional optimization techniques rely on high-fidelity simulations using finite element analysis (FEA) and thermal modeling, which are computationally expensive and time-consuming. Deep learning offers a compelling solution via the potential to create rapid, very accurate surrogate models that replicate simulation response and allow fast design exploration. The chapter presents a comprehensive overview of the use of deep neural networks (DNNs) utilized in surrogate modeling and optimization of electrical machines. The methodology includes architecture design, activation functions, training, and performance measures, with functional MATLAB examples provided throughout. A case study involving efficiency optimization of a brushless DC (BLDC) motor is employed to demonstrate model tuning, validation, and deployment. Closing the design simulation loop with real-time feedback, deep learning not only accelerates design cycles but also enables intelligent, adaptive design paradigms for next-generation electrical machines.