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Surrogate models expedite the search for promising designs bystanding in for expensive design evaluations or simulations. Theyprovide a global model of some metric of a design (such as weight,aerodynamic drag, cost, etc.), which can then be optimizedefficiently. Engineering Design via Surrogate Modelling is aself-contained guide to surrogate models and their use inengineering design. The fundamentals of building, selecting,validating, searching and refining a surrogate are presented in amanner accessible to novices in the field. Figures are usedliberally to explain the key concepts and clearly…mehr

Produktbeschreibung
Surrogate models expedite the search for promising designs bystanding in for expensive design evaluations or simulations. Theyprovide a global model of some metric of a design (such as weight,aerodynamic drag, cost, etc.), which can then be optimizedefficiently. Engineering Design via Surrogate Modelling is aself-contained guide to surrogate models and their use inengineering design. The fundamentals of building, selecting,validating, searching and refining a surrogate are presented in amanner accessible to novices in the field. Figures are usedliberally to explain the key concepts and clearly show thedifferences between the various techniques, as well as to emphasizethe intuitive nature of the conceptual and mathematical reasoningbehind them. More advanced and recent concepts are each presented instand-alone chapters, allowing the reader to concentrate onmaterial pertinent to their current design problem, and conceptsare clearly demonstrated using simple design problems. Thiscollection of advanced concepts (visualization, constrainthandling, coping with noisy data, gradient-enhanced modelling,multi-fidelity analysis and multiple objectives) represents aninvaluable reference manual for engineers and researchers active inthe area. Engineering Design via Surrogate Modelling iscomplemented by a suite of Matlab codes, allowing the reader toapply all the techniques presented to their own design problems. Byapplying statistical modelling to engineering design, this bookbridges the wide gap between the engineering and statisticscommunities. It will appeal to postgraduates and researchers acrossthe academic engineering design community as well as practisingdesign engineers. * Provides an inclusive and practical guide to using surrogatesin engineering design. * Presents the fundamentals of building, selecting, validating,searching and refining a surrogate model. * Guides the reader through the practical implementation of asurrogate-based design process using a set of case studies fromreal engineering design challenges. Accompanied by a companion website featuring Matlab softwareat http://www.wiley.com/go/forrester

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 228
  • Erscheinungstermin: 15. September 2008
  • Englisch
  • ISBN-13: 9780470770795
  • Artikelnr.: 37299393
Autorenporträt
Dr. Alexander I. J. Forrester is Lecturer in Engineering Design at the University of Southampton. His main area of research focuses on improving the efficiency with which expensive analysis (particularly computational fluid dynamics) is used in design. His techniques have been applied to wing aerodynamics, satellite structures, sports equipment design and Formula One. Dr Andras Sobester is a Lecturer and EPSRC/ Royal Academy of Engineering research Fellow in the School of Engineering Sciences at the University of Southampton. His research interests include aircraft design, aerodynamic shape parameterization and optimization, as well as engineering design technology in general. Professor Andy J. Keane currently holds the Chair of Computational Engineering at the University of Southampton. He leads the University's Computational Engineering at the Design Research Group and directs the rolls-Royce University Technology centre for Computational Engineering. His interests lie primarily in the aerospace sciences, with a focus on the design of aerospace systems using computational methods. He has published over two hundred papers and three books in this area, many of which deal with surrogate modelling concepts.
Inhaltsangabe
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. 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. References. 2. 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. 2.5.1 The Support Vector Predictor. 2.5.2 The Kernel Trick. 2.5.3 Finding the Support Vectors. 2.5.4 Finding µ. 2.5.5 Choosing C and µ. 2.5.6 Computing µ: v
SVR 71. 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. 3.3.4 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. 5.4.1 Expected Improvement With Simple Penalty Function. 5.4.2 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. 7.1.2 Complex Step Approximation. 7.1.3 Adjoint Methods and Algorithmic Differentiation. 7.2 Gradient
enhanced Modelling. 7.3 Hessian
enhanced Modelling. 7.4 Summary. References. 8. Multifidelity Analysis. 8.1 Co
Kriging. 8.2 One
variable demonstration. 8.3 Choosing Xc and Xe. 8.4 Summary. References. 9. Multiple Design Objectives. 9.1 Pareto Optimization. 9.2 Multiobjective Expected Improvement. 9.3 Design of the Nowacki Cantilever Beam Using Multiobjective, Constrained Expected Improvement. 9.4 Design of a Helical Compression Spring Using Multiobjective, Constrained Expected Improvement. 9.5 Summary. References. Appendix: Example Problems. A.1 One
Variable Test Function. A.2 Branin Test Function. A.3 Aerofoil Design. A.4 The Nowacki Beam. A.5 Multiobjective, Constrained Optimal Design of a Helical Compression Spring. A.6 Novel Passive Vibration Isolator Feasibility. References. Index.