20.500.12556/DKUM-94206 Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networks Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%. interior permanent magnet synchronous machine artificial neural network metamodel multi-objective optimization finite element method močnostni sistemi električna napetost nadzorovano učenje nenadzorovano učenje true false true Elsevier Science Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence © 2025 The Authors Članek v reviji 2025-08-08 09:54:16 2025-08-08 09:54:16 2025-08-09 03:20:03 0000-00-00 00:00:00 2025 0 0 15 str. vol. 160, [article no.] 111753 15 Nov. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-15 2025-07-08 2025-07-25 621.31 244872195 10.1016/j.engappai.2025.111753 1873-6769 1-s2.0-S0952197625017555-main.pdf 1-s2.0-S0952197625017555-main.pdf 1 7C98EC9FF2E83BCEFDA518A48D63F8B1 e582caee1f7832ba48e1614bd09d7b858781c3f33633004280a24a3b8b2f4b5f 4c29c899-742d-11f0-bafb-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=194941 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0