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図書

図書
Ashok D. Belegundu, Tirupathi R. Chandrupatla
出版情報: Cambridge : Cambridge University Press, 2019  xvi, 449 p. ; 26 cm
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図書

図書
[by] Wilfred E. Baker, Peter S. Westine, and Franklin T. Dodge
出版情報: Rochelle Park, N.J. : Spartan Books; [distributed by] Hayden Book Co, [1973]  396 p ; 24 cm
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3.

図書

図書
Wilfred E. Baker, Peter S. Westine, Franklin T. Dodge
出版情報: Amsterdam ; Tokyo : Elsevier, 1991  xi, 384 p. ; 25 cm
シリーズ名: Fundamental studies in engineering ; 12
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4.

図書

図書
Dieterich J. Schuring
出版情報: Oxford ; New York : Pergamon Press, c1977  xii, 299 p. ; 25 cm
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5.

図書

図書
P.E. Wellstead
出版情報: London ; New York : Academic Press, 1979  ix, 279 p. ; 24 cm
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6.

図書

図書
L.I. Sedov ; translation edited by Maurice Holt ; translation by Morris Friedman
出版情報: New York : Academic Press , London : Infosearch, 1959  xvi, 363 p. ; 24 cm
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7.

図書

図書
Ashok D. Belegundu, Tirupathi R. Chandrupatla
出版情報: Cambridge : Cambridge University Press, 2011  xii, 463 p. ; 24 cm.
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目次情報: 続きを見る
Preliminary concepts / 1:
One dimensional unconstrained minimization / 2:
Unconstrained optimization / 3:
Linear programming / 4:
Constrained minimization / 5:
Penalty functions, duality, and geometric programming / 6:
Direct search methods for nonlinear optimization / 7:
Multiobjective optimization / 8:
Integer and discrete programming / 9:
Dynamic programming / 10:
Optimization applications for transportation, assignment, and network problems / 11:
Finite element based optimization / 12:
Preliminary concepts / 1:
One dimensional unconstrained minimization / 2:
Unconstrained optimization / 3:
8.

図書

図書
F.W. David and H. Nolle
出版情報: London ; Boston : Butterworths, 1982  185 p. ; 25 cm
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9.

図書

図書
Alexander I.J. Forrester, András Sóbester, and Andy J. Keane
出版情報: Chichester : John Wiley, c2008  xviii, 210 p., [8] leaves of plates ; 25 cm
シリーズ名: Progress in astronautics and aeronautics ; v. 226
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目次情報: 続きを見る
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
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