Electrical Machine

Electrical machine design optimization is a computationally intensive multi-objective problem aiming to achieve optimal performance metrics like torque and efficiency while adhering to various constraints. Current research heavily utilizes machine learning, particularly deep neural networks (including variational autoencoders) and reinforcement learning, to create surrogate models that drastically reduce the computational burden of finite element analysis, enabling faster and more efficient design exploration. This accelerates the design process, allowing for the exploration of a wider design space and ultimately leading to improved machine performance and reduced development costs across various machine topologies.