Finite Elements-based Optimization
Electromagnetic Product Design and Nondestructive Evaluation


Language: Anglais
Cover of the book Finite Elements-based Optimization

Subjects for Finite Elements-based Optimization

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· 15.6x23.5 cm · Hardback

This book is intended to be a cookbook for students and researchers to understand the finite element method and optimization methods and couple them to effect shape optimization. The optimization part of the book will survey optimization methods and focus on the genetic algorithm and Powell?s method for implementation in the codes. It will contain pseudo-code for the relevant algorithms and homework problems to reinforce the theory to compile finite element programs capable of shape optimization.


  • Enables readers to understand the finite element method and optimization methods and couple them to effect shape optimization
  • Presents simple approach with algorithms for synthesis
  • Focuses on automated computer aided design (CAD) of electromagnetic devices
  • Provides a unitary framework involving optimization and numerical modelling
  • Discusses how to integrate open-source mesh generators into your code
  • Indicates how parallelization of algorithms, especially matrix solution and optimization, may be approached cheaply using the graphics processing unit (GPU) that is available on most PCs today
  • Includes coupled problem optimization using hyperthermia as an example
The Finite Element Method. In One-dimension. In Two dimensions. In Three dimesnions. Visualization and Postprocessing. Mesh
Generation and Open Source Software. High Order Elements. The Galerkin Method. Competing Methods. The Finite Difference
Method. The Boundary Element Method. Why Finite Elements. Mesh Generation and Postprocessing for Finite Elements.
Matrix Computation with Sparse Matrices. Gauss Iterations. Gaussian Triangulation. Cholesky Decomposition. Profile Storage.
Conjugate Gradient. Sparse Storage.Finite Elements in Electric Machines. High Frequency Problems and Vector Elements.
Optimization Methods. Formulation with Constraints. Search Methods. Zeroth Order Methods. Steepest Descent. Conjugate
Gradients. Multi-object Optimization. AI Techniques in Finite Element Optimization. Case Studies from Electromagnetic Product
Design. Case Studies from Nondestructive Evaluation.

S. Ratnajeevan H. Hoole, B.Sc. Eng. Hons Cey., M.Sc. with Mark of Distinction London, Ph.D. Carnegie Mellon, is Professor of Electrical and Computer Engineering at Michigan State University in the US. For his accomplishments in electromagnetic product synthesis the University of London awarded him its higher doctorate, the D.Sc. (Eng.) degree, in 1993, and the IEEE elevated him to the grade of Fellow in 1995 with the citation "For contributions to computational methods for design optimization of electrical devices." His paper on using his inverse problem methods from design for NDE is widely cited, as is his paper on neural networks for the same purpose. These appear in The IEEE Transactions on Magnetics (1991 and 1993, respectively). He has authored 5 engineering texts published by Elsevier, another by Elsevier now carried by Prentice Hall, Oxford, Cambridge (India) and WIT Press.

Prof. Hoole has been Vice Chancellor of University of Jaffna in Sri Lanka, and as Member of the University Grants Commission there, was responsible with six others for the regulation of the administration and academic standards of all 15 Sri Lankan universities and their admissions and funding. He has contributed widely to the learned literature on Tamil studies and been a regular columnist in newspapers. Prof. Hoole has been trained in Human Rights Research and Teaching at The René Cassin International Institute of Human Rights, Strasbourg, France, and has pioneered teaching human rights in the engineering curriculum.

Yovahn Y. Ratnajeevan Hoole is a graduate student at the University of Illinois at Urbana Champaign. He holds a B.S. in Computer Science and a B.A. in Electrical Engineering from Rice University. He is currently working towards a doctorate in Electrical Engineering. His research interests are in Optimization, Machine Learning and their applications to real world engineering problems.