Surrogate-Based Modeling and Optimization, 1st Edition

  • Published By:
  • ISBN-10: 1461475511
  • ISBN-13: 9781461475514
  • DDC: 620.00151
  • Grade Level Range: College Freshman - College Senior
  • 412 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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Contemporary engineering design is heavily based on computer simulations. Accurate, high-fidelity simulations are used not only for design verification but, even more importantly, to adjust parameters of the system to have it meet given performance requirements. Unfortunately, accurate simulations are often computationally very expensive with evaluation times as long as hours or even days per design, making design automation using conventional methods impractical. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest but they are much more reasonable and analytically tractable.  This volume features surrogate-based modeling and optimization techniques, and their applications for solving difficult and computationally expensive engineering design problems. It begins by presenting the basic concepts and formulations of the surrogate-based modeling and optimization paradigm and then discusses relevant modeling techniques, optimization algorithms and design procedures, as well as state-of-the-art developments. The chapters are self-contained with basic concepts and formulations along with applications and examples. The book will be useful to researchers in engineering and mathematics, in particular those who employ computationally heavy simulations in their design work.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Space Mapping for Electromagnetic-Simulation-Driven Design Optimization.
2: Surrogate-Based Circuit Design Centering.
3: Simulation-Driven Antenna Design Using Surrogate-Based Optimization.
4: Practical Application of Space Mapping Techniques to the Synthesis of CSRR-Based Artificial Transmission Lines.
5: The Efficiency of Difference Mapping in Space Mapping-Based Optimization.
6: Bayesian Support Vector Regression Modeling of Microwave Structures for Design Applications.
7: Artificial Neural Networks and Space Mapping for EM-Based Modeling and Design of Microwave Circuits.
8: Model-Based Variation-Aware Integrated Circuit Design.
9: Computing Surrogates for Gas Network Simulation Using Model Order Reduction.
10: Aerodynamic Shape Optimization by Space Mapping.
11: Efficient Robust Design with Stochastic Expansions.
12: Surrogate Models for Aerodynamic Shape Optimisation.
13: Knowledge-Based Surrogate Modeling in Engineering Design Optimization.
14: Switching Response Surface Models for Structural Health Monitoring of Bridges.
15: Surrogate Modeling of Stability Constraints for Optimization of Composite Structures.
16: Engineering Optimization and Industrial Applications.