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Simulation-Driven Design by Knowledge-Based Response Correction Techniques

  • Book
  • © 2016

Overview

Authors:
  1. Slawomir Koziel

    1. Eng. Optimization & Modeling Center, Reykjavik University, Reykjavik, Iceland

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    You can also search for this author in PubMed   Google Scholar

  2. Leifur Leifsson

    1. College of Engineering, Iowa State University, Ames, USA

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    You can also search for this author in PubMed   Google Scholar

  • Focused on efficient simulation-driven multi-fidelity optimization techniques

  • Presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response

  • Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included

  • Includes more than 100 high-resolution diagrams and figures

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction

    • Slawomir Koziel, Leifur Leifsson
    Pages 1-6

  3. Simulation-Driven Design

    • Slawomir Koziel, Leifur Leifsson
    Pages 7-13

  4. Fundamentals of Numerical Optimization

    • Slawomir Koziel, Leifur Leifsson
    Pages 15-29

  5. Introduction to Surrogate Modeling and Surrogate-Based Optimization

    • Slawomir Koziel, Leifur Leifsson
    Pages 31-61

  6. Design Optimization Using Response Correction Techniques

    • Slawomir Koziel, Leifur Leifsson
    Pages 63-74

  7. Surrogate-Based Optimization Using Parametric Response Correction

    • Slawomir Koziel, Leifur Leifsson
    Pages 75-98

  8. Nonparametric Response Correction Techniques

    • Slawomir Koziel, Leifur Leifsson
    Pages 99-129

  9. Expedited Simulation-Driven Optimization Using Adaptively Adjusted Design Specifications

    • Slawomir Koziel, Leifur Leifsson
    Pages 131-146

  10. Surrogate-Assisted Design Optimization Using Response Features

    • Slawomir Koziel, Leifur Leifsson
    Pages 147-163

  11. Enhancing Response Correction Techniques by Adjoint Sensitivity

    • Slawomir Koziel, Leifur Leifsson
    Pages 165-191

  12. Multi-objective Optimization Using Variable-Fidelity Models and Response Correction

    • Slawomir Koziel, Leifur Leifsson
    Pages 193-210

  13. Physics-Based Surrogate Modeling Using Response Correction

    • Slawomir Koziel, Leifur Leifsson
    Pages 211-243

  14. Summary and Discussion

    • Slawomir Koziel, Leifur Leifsson
    Pages 245-247

  15. Back Matter

    Pages 249-262
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Keywords

About this book

Focused on efficient simulation-driven multi-fidelity optimization techniques, this monograph on simulation-driven optimization covers simulations utilizing physics-based low-fidelity models, often based on coarse-discretization simulations or other types of simplified physics representations, such as analytical models. The methods presented in the book exploit as much as possible any knowledge about the system or device of interest embedded in the low-fidelity model with the purpose of reducing the computational overhead of the design process. Most of the techniques described in the book are of response correction type and can be split into parametric (usually based on analytical formulas) and non-parametric, i.e., not based on analytical formulas. The latter, while more complex in implementation, tend to be more efficient.

The book presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included. The book demonstrates the use of the discussed techniques for solving real-world engineering design problems, including applications in microwave engineering, antenna design, and aero/hydrodynamics.

Authors and Affiliations

  • Eng. Optimization & Modeling Center, Reykjavik University, Reykjavik, Iceland

    Slawomir Koziel

  • College of Engineering, Iowa State University, Ames, USA

    Leifur Leifsson

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