LEADER 02013nam 2200421 n 450 001 996394525103316 005 20200824121820.0 035 $a(CKB)4940000000121840 035 $a(EEBO)2240884035 035 $a(UnM)99859074e 035 $a(UnM)99859074 035 $a(EXLCZ)994940000000121840 100 $a19850412d1644 uh | 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aThree ordinances of the Lords and Commons assembled in Parliament$b[electronic resource]$efirst for the regulating of the excise, upon all manner of salt, and flesh, viz beeves, muttons, veales, porks, lambs, and other butchers meat kild for provision of victuals. The second, for the constant payment of 200 li. a weeke, towards the maintenance of maimed and vvounded souldiers, and relieving their wives and children, and widdowes whose husbands are slaine in the service of the Parliament and other great affaires of the Common-wealth. The third, being an explanation of the late ordinance of excise upon iron, tynne, hops, hats, Monmouth caps, allom and copperas, &c 210 $a[London?] $cAugust 5.Printed for John Wright in the Old Bayley$d1644 215 $a[2], 6 p 300 $aReproduction of original in the British Library. 330 $aeebo-0018 606 $aSalt$xTaxation$zEngland$vEarly works to 1800 606 $aMeat$xTaxation$zEngland$vEarly works to 1800 606 $aMines and mineral resources$xTaxation$zEngland$vEarly works to 1800 606 $aMilitary pensions$zEngland$vEarly works to 1800 606 $aExcise tax$zEngland$vEarly works to 1800 615 0$aSalt$xTaxation 615 0$aMeat$xTaxation 615 0$aMines and mineral resources$xTaxation 615 0$aMilitary pensions 615 0$aExcise tax 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996394525103316 996 $aThree ordinances of the Lords and Commons assembled in Parliament$92309766 997 $aUNISA LEADER 01032nam0 22002651i 450 001 UON00188786 005 20231205103205.585 100 $a20030730d1987 |0itac50 ba 101 $arus 102 $aSU 105 $a|||| 1|||| 200 1 $aNikolaj Zabolockij$e?izn', tvor?estvo, metamorfozy$fA. 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Forrester, Andras Sobester and Andy J. Keane 210 1$aChichester, West Sussex, United Kingdom :$cWiley,$d2008. 215 $a1 online resource (xviii, 210 pages) $cillustrations (some colour) 311 1 $a9780470060681 311 1 $a0470060689 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 $aEnginyeria$xDisseny$xModels matemàtics$2lemac 606 $aEnginyeria$xDisseny$xMètodes estadístics$2lemac 615 0$aEngineering design$xMathematical models. 615 0$aEngineering design$xStatistical methods. 615 7$aEnginyeria$xDisseny$xModels matemàtics. 615 7$aEnginyeria$xDisseny$xMètodes estadístics. 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 906 $aBOOK 912 $a9911020015003321 996 $aEngineering design via surrogate modelling$94040700 997 $aUNINA