05931oam 2200733 450 991083096920332120200925212234.00-470-77080-51-281-84101-397866118410101-61583-477-X0-470-77079-1(CKB)1000000000551097(EBL)366798(OCoLC)264714649(SSID)ssj0000147488(PQKBManifestationID)11910443(PQKBTitleCode)TC0000147488(PQKBWorkID)10011310(PQKB)10160729(MiAaPQ)EBC366798(PPN)223474088(EXLCZ)99100000000055109720080414h20082008 uy 0engur|n|||||||||txtrdacontentcrdamediacrrdacarrierEngineering design via surrogate modelling a practical guide /Alexander I.J. Forrester, András Sóbester, and Andy J. Keane, University of Southampton, UKChichester, West Sussex, England :Wiley,2008.©20081 online resource (xviii, 210 pages) illustrations (some colour)0-470-06068-9 Original 9780470060681 0470060689 9780470770801 0470770805 9781563479557 1563479559 (DLC) 2008017093 (OCoLC)225090909 Includes bibliographical references and index.Engineering Design via Surrogate Modelling; Contents; Preface; About the Authors; Foreword; Prologue; Part I Fundamentals; 1 Sampling Plans; 1.1 The 'Curse of Dimensionality' and How to Avoid It; 1.2 Physical versus Computational Experiments; 1.3 Designing Preliminary Experiments (Screening); 1.3.1 Estimating the Distribution of Elementary Effects; 1.4 Designing a Sampling Plan; 1.4.1 Stratification; 1.4.2 Latin Squares and Random Latin Hypercubes; 1.4.3 Space-filling Latin Hypercubes; 1.4.4 Space-filling Subsets; 1.5 A Note on Harmonic Responses; 1.6 Some Pointers for Further ReadingReferences2 Constructing a Surrogate; 2.1 The Modelling Process; 2.1.1 Stage One: Preparing the Data and Choosing a Modelling Approach; 2.1.2 Stage Two: Parameter Estimation and Training; 2.1.3 Stage Three: Model Testing; 2.2 Polynomial Models; 2.2.1 Example One: Aerofoil Drag; 2.2.2 Example Two: a Multimodal Testcase; 2.2.3 What About the k-variable Case?; 2.3 Radial Basis Function Models; 2.3.1 Fitting Noise-Free Data; 2.3.2 Radial Basis Function Models of Noisy Data; 2.4 Kriging; 2.4.1 Building the Kriging Model; 2.4.2 Kriging Prediction; 2.5 Support Vector Regression2.5.1 The Support Vector Predictor2.5.2 The Kernel Trick; 2.5.3 Finding the Support Vectors; 2.5.4 Finding ; 2.5.5 Choosing C and ; 2.5.6 Computing : -SVR; 2.6 The Big(ger) Picture; References; 3 Exploring and Exploiting a Surrogate; 3.1 Searching the Surrogate; 3.2 Infill Criteria; 3.2.1 Prediction Based Exploitation; 3.2.2 Error Based Exploration; 3.2.3 Balanced Exploitation and Exploration; 3.2.4 Conditional Likelihood Approaches; 3.2.5 Other Methods; 3.3 Managing a Surrogate Based Optimization Process; 3.3.1 Which Surrogate for What Use?3.3.2 How Many Sample Plan and Infill Points?3.3.3 Convergence Criteria; 3.4 Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking; References; Part II Advanced Concepts; 4 Visualization; 4.1 Matrices of Contour Plots; 4.2 Nested Dimensions; Reference; 5 Constraints; 5.1 Satisfaction of Constraints by Construction; 5.2 Penalty Functions; 5.3 Example Constrained Problem; 5.3.1 Using a Kriging Model of the Constraint Function; 5.3.2 Using a Kriging Model of the Objective Function; 5.4 Expected Improvement Based Approaches5.4.1 Expected Improvement With Simple Penalty Function5.4.2 Constrained Expected Improvement; 5.5 Missing Data; 5.5.1 Imputing Data for Infeasible Designs; 5.6 Design of a Helical Compression Spring Using Constrained Expected Improvement; 5.7 Summary; References; 6 Infill Criteria with Noisy Data; 6.1 Regressing Kriging; 6.2 Searching the Regression Model; 6.2.1 Re-Interpolation; 6.2.2 Re-Interpolation With Conditional Likelihood Approaches; 6.3 A Note on Matrix Ill-Conditioning; 6.4 Summary; References; 7 Exploiting Gradient Information; 7.1 Obtaining Gradients; 7.1.1 Finite Differencing7.1.2 Complex Step ApproximationSurrogate models expedite the search for promising designs by standing in for expensive design evaluations or simulations. They provide a global model of some metric of a design (such as weight, aerodynamic drag, cost, etc.), which can then be optimized efficiently. Engineering Design via Surrogate Modelling is a self-contained guide to surrogate models and their use in engineering design. The fundamentals of building, selecting, validating, searching and refining a surrogate are presented in a manner accessible to novices in the field. Figures are used liberally to explain the keyEngineering designMathematical modelsEngineering designStatistical methodsEngineering designStatistical methodsfast(OCoLC)fst00910488Engineering designMathematical modelsfast(OCoLC)fst00910477Engineering designMathematical models.Engineering designStatistical methods.Engineering designStatistical methods.Engineering designMathematical models.620.0044620/.0042015118Forrester Alexander I. J.1675328Sóbester AndrásKeane A. J.MiAaPQMiAaPQMiAaPQNhCcYBPBOOK9910830969203321Engineering design via surrogate modelling4040700UNINA