Efficient E-Machine Design: Enhancing Scalability with Transfer Learning and Multi-Fidelity Data
Amir Akbari, David Alister Lowther · 2025
This paper investigates the use of multi-fidelity datasets combined with transfer learning to accelerate the design of electric machines (E-machines). By reusing knowledge from prior tasks, transfer learning reduces the cost and time of creating datasets and training models. A transfer-learning-enhanced conditional GAN (TLE-CDC-GAN) is employed, trained on multi-fidelity simulation data to generate E-machine designs conditioned on torque specifications. The results show that transfer learning enhances efficiency, generalizes well across tasks, and works with smaller and low-fidelity datasets. This provides a scalable and cost-effective framework for solving inverse design problems in E-machine development, giving earlier insights for designers and avoiding the frustration of starting over if the design goals change.