LEADER 05931oam 2200733 450 001 9910830969203321 005 20200925212234.0 010 $a0-470-77080-5 010 $a1-281-84101-3 010 $a9786611841010 010 $a1-61583-477-X 010 $a0-470-77079-1 035 $a(CKB)1000000000551097 035 $a(EBL)366798 035 $a(OCoLC)264714649 035 $a(SSID)ssj0000147488 035 $a(PQKBManifestationID)11910443 035 $a(PQKBTitleCode)TC0000147488 035 $a(PQKBWorkID)10011310 035 $a(PQKB)10160729 035 $a(MiAaPQ)EBC366798 035 $a(PPN)223474088 035 $a(EXLCZ)991000000000551097 100 $a20080414h20082008 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEngineering design via surrogate modelling $ea practical guide /$fAlexander I.J. Forrester, Andra?s So?bester, and Andy J. Keane, University of Southampton, UK 210 1$aChichester, West Sussex, England :$cWiley,$d2008. 210 4$dİ2008 215 $a1 online resource (xviii, 210 pages) $cillustrations (some colour) 311 $a0-470-06068-9 311 08$aOriginal 9780470060681 0470060689 9780470770801 0470770805 9781563479557 1563479559 (DLC) 2008017093 (OCoLC)225090909 320 $aIncludes bibliographical references and index. 327 $aEngineering 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 Reading 327 $aReferences2 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 Regression 327 $a2.5.1 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? 327 $a3.3.2 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 Approaches 327 $a5.4.1 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 Differencing 327 $a7.1.2 Complex Step Approximation 330 $aSurrogate 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 key 606 $aEngineering design$xMathematical models 606 $aEngineering design$xStatistical methods 606 $aEngineering design$xStatistical methods$2fast$3(OCoLC)fst00910488 606 $aEngineering design$xMathematical models$2fast$3(OCoLC)fst00910477 615 0$aEngineering design$xMathematical models. 615 0$aEngineering design$xStatistical methods. 615 7$aEngineering design$xStatistical methods. 615 7$aEngineering design$xMathematical models. 676 $a620.0044 676 $a620/.0042015118 700 $aForrester$b Alexander I. J.$01675328 702 $aSo?bester$b Andra?s 702 $aKeane$b A. J. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 801 2$bNhCcYBP 906 $aBOOK 912 $a9910830969203321 996 $aEngineering design via surrogate modelling$94040700 997 $aUNINA