Engineering design via surrogate modelling : a practical guide

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Engineering design via surrogate modelling : a practical guide / Alexander I.J. Forrester, András Sóbester, and Andy J. Keane

資料種別:

図書

出版情報:

Chichester : John Wiley, c2008

形態:

xviii, 210 p., [8] leaves of plates ; 25 cm

シリーズ名:

Progress in astronautics and aeronautics ; v. 226 <BA00087068>

著者名:

ISBN:

9781563479557 [1563479559] (hbk.)

書誌ID:

BA90020144

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注記:

Copublished by the American Institute of Aeronautics and Astronautics

主題:

Engineering design; Engineering models

言語:

英語

目次情報:

Preface

About the Authors

Foreword

Prologue

Fundamentals / Part I：

Sampling Plans / 1：

The 'Curse of Dimensionality' and How to Avoid It / 1.1：

Physical versus Computational Experiments / 1.2：

Designing Preliminary Experiments (Screening) / 1.3：

Estimating the Distribution of Elementary Effects / 1.3.1：

Designing a Sampling Plan / 1.4：

Stratification / 1.4.1：

Latin Squares and Random Latin Hypercubes / 1.4.2：

Space-filling Latin Hypercubes / 1.4.3：

Space-filling Subsets / 1.4.4：

A Note on Harmonic Responses / 1.5：

Some Pointers for Further Reading / 1.6：

References

Constructing a Surrogate / 2：

The Modelling Process / 2.1：

Stage One: Preparing the Data and Choosing a Modelling Approach / 2.1.1：

Stage Two: Parameter Estimation and Training / 2.1.2：

Stage Three: Model Testing / 2.1.3：

Polynomial Models / 2.2：

Example One: Aerofoil Drag / 2.2.1：

Example Two: a Multimodal Testcase / 2.2.2：

What About the k-variable Case? / 2.2.3：

Radial Basis Function Models / 2.3：

Fitting Noise-Free Data / 2.3.1：

Radial Basis Function Models of Noisy Data / 2.3.2：

Kriging / 2.4：

Building the Kriging Model / 2.4.1：

Kriging Prediction / 2.4.2：

Support Vector Regression / 2.5：

The Support Vector Predictor / 2.5.1：

The Kernel Trick / 2.5.2：

Finding the Support Vectors / 2.5.3：

Finding [mu] / 2.5.4：

Choosing C and [epsilon] / 2.5.5：

Computing [epsilon]: v-SVR / 2.5.6：

The Big(ger) Picture / 2.6：

Exploring and Exploiting a Surrogate / 3：

Searching the Surrogate / 3.1：

Infill Criteria / 3.2：

Prediction Based Exploitation / 3.2.1：

Error Based Exploration / 3.2.2：

Balanced Exploitation and Exploration / 3.2.3：

Conditional Likelihood Approaches / 3.2.4：

Other Methods / 3.2.5：

Managing a Surrogate Based Optimization Process / 3.3：

Which Surrogate for What Use? / 3.3.1：

How Many Sample Plan and Infill Points? / 3.3.2：

Convergence Criteria / 3.3.3：

Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking / 3.4：

Advanced Concepts / Part II：

Visualization / 4：

Matrices of Contour Plots / 4.1：

Nested Dimensions / 4.2：

Reference

Constraints / 5：

Satisfaction of Constraints by Construction / 5.1：

Penalty Functions / 5.2：

Example Constrained Problem / 5.3：

Using a Kriging Model of the Constraint Function / 5.3.1：

Using a Kriging Model of the Objective Function / 5.3.2：

Expected Improvement Based Approaches / 5.4：

Expected Improvement With Simple Penalty Function / 5.4.1：

Constrained Expected Improvement / 5.4.2：

Missing Data / 5.5：

Imputing Data for Infeasible Designs / 5.5.1：

Design of a Helical Compression Spring Using Constrained Expected Improvement / 5.6：

Summary / 5.7：

Infill Criteria with Noisy Data / 6：

Regressing Kriging / 6.1：

Searching the Regression Model / 6.2：

Re-Interpolation / 6.2.1：

Re-Interpolation With Conditional Likelihood Approaches / 6.2.2：

A Note on Matrix Ill-Conditioning / 6.3：

Exploiting Gradient Information / 6.4：

Obtaining Gradients / 7.1：

Finite Differencing / 7.1.1：

Complex Step Approximation / 7.1.2：

Adjoint Methods and Algorithmic Differentiation / 7.1.3：

Gradient-enhanced Modelling / 7.2：

Hessian-enhanced Modelling / 7.3：

Multi-fidelity Analysis / 7.4：

Co-Kriging / 8.1：

One-variable Demonstration / 8.2：

Choosing X[subscript c] and X[subscript e] / 8.3：

Multiple Design Objectives / 8.4：

Pareto Optimization / 9.1：

Multi-objective Expected Improvement / 9.2：

Design of the Nowacki Cantilever Beam Using Multi-objective, Constrained Expected Improvement / 9.3：

Design of a Helical Compression Spring Using Multi-objective, Constrained Expected Improvement / 9.4：

Example Problems / 9.5：

One-Variable Test Function / A.1：

Branin Test Function / A.2：

Aerofoil Design / A.3：

The Nowacki Beam / A.4：

Multi-objective, Constrained Optimal Design of a Helical Compression Spring / A.5：

Novel Passive Vibration Isolator Feasibility / A.6：

Index

Preface

About the Authors

Foreword

Prologue

Fundamentals / Part I：

Sampling Plans / 1：

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