Academic Press Topology Optimization and AI-based Design of Power Electronic and Electrical Devices: Principles and Methods

Topology optimization and AI-based design of power electronic and electrical devices provides an essential foundation in the emergent design methodology as it moves toward commercial development, including electrical devices as traction motors for electric motors, transformers, inductors, reactors, and power electronics circuits. Opening with an introduction to electromagnetism and computational electromagnetics for optimal design, this book outlines principles and foundations in finite element methods and illustrates numerical techniques useful for finite element analysis. It summarizes the foundations of deterministic and stochastic optimization methods, including genetic algorithm, CMA-ES, and simulated annealing for quantum and quantum-inspired optimization, alongside representative algorithms. The book goes on to discuss parameter optimization and topology optimization of electrical devices alongside current implementations including magnetic shields, 2D and 3D models of electric motors, and wireless power transfer devices. Finally, it concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, Bayesian optimization, direct inverse modeling, deep neural networks, explainable AI, variational autoencoder, and integrated design methods using Monte Carlo tree searches for electrical devices and circuits.   Assists researchers and design engineers in applying emergent topology design optimization to power electronics and electrical device design, supported by step-by-step methods, heuristic derivation, and pseudocodes Proposes unique formulations of AI-based design for electrical devices using Monte Carlo tree search and other machine learning methods Is richly accompanied by detailed numerical examples and repletes with computational support materials in algorithms and explanatory formulae Includes access to pedagogical videos on topics including the evolutionary process of topology optimization, the distribution of genetic algorithms, and CMA-ES