by Hajime Igarashi
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
