Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?

The use of metamodeling techniques in the design and analysis of computer experiments has progressed remarkably in the past 25 years, but how far has the field really come? This is the question addressed in this paper, namely, the extent towhich the use ofmetamodeling techniques inmultidisciplinary design optimization have evolved in the 25 years since the seminal paper on design and analysis of computer experiments by Sacks et al. (“Design and Analysis of Computer Experiments,” Statistical Science, Vol. 4, No. 4, 1989, pp. 409–435). Rather than a technical review of the entire body of metamodeling literature, the focus is on the evolution and motivation for advancements in metamodeling with some discussion on the research itself; not surprisingly, much of the current research motivation is the same as it was in the past. Based on current research thrusts in the field, multifidelity approximations and ensembles (i.e., sets) of metamodels, as well as the availability of metamodels within commercial software, are emphasized. Design space exploration and visualization via metamodels are also presented as they rely heavily onmetamodels for rapid design evaluations during exploration. The closing remarks offer insight into future research directions, mostly motivated by the need for new capabilities and the ability to handle more complex simulations.

[1]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[2]  Richard J. Balling,et al.  Approximation of Computationally Expensive and Noisy Functions for Constrained Nonlinear Optimization , 1987 .

[3]  K. R. Krishnanand,et al.  Optimal appliance scheduling in building operating systems for cost-effective energy management , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[4]  Michael S. Eldred,et al.  Second-Order Corrections for Surrogate-Based Optimization with Model Hierarchies , 2004 .

[5]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[6]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[7]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[8]  John W. Bandler,et al.  Space mapping technique for electromagnetic optimization , 1994 .

[9]  L. Watson,et al.  How to Decide Whether to Run One More Cycle in Efficient Global Optimization , 2012 .

[10]  J. H. Starnes,et al.  Construction of Response Surface Approximations for Design Optimization , 1998 .

[11]  Richard H. Crawford,et al.  Multidimensional sequential sampling for NURBs-based metamodel development , 2007, Engineering with Computers.

[12]  J. Renaud,et al.  Approximation in nonhierarchic system optimization , 1994 .

[13]  Tomas Jansson,et al.  Optimization of Draw-In for an Automotive Sheet Metal Part An evaluation using surrogate models and response surfaces , 2005 .

[14]  Weiyu Liu,et al.  GRADIENT-ENHANCED NEURAL NETWORK RESPONSE SURFACE APPROXIMATIONS , 2000 .

[15]  Layne T. Watson,et al.  Efficient global optimization algorithm assisted by multiple surrogate techniques , 2012, Journal of Global Optimization.

[16]  K Sudhakar,et al.  Customized Regression Model for Improving Low Fidelity Analysis Tool , 2006 .

[17]  S. Koziel,et al.  Space-Mapping Optimization With Adaptive Surrogate Model , 2007, IEEE Transactions on Microwave Theory and Techniques.

[18]  Yuhong Yang REGRESSION WITH MULTIPLE CANDIDATE MODELS: SELECTING OR MIXING? , 1999 .

[19]  Shawn E. Gano,et al.  Update strategies for kriging models used in variable fidelity optimization , 2006 .

[20]  Vassili Toropov,et al.  STRUCTURAL OPTIMIZATION USING APPROXIMATIONS BASED ON SIMPLIFIED NUMERICAL MODELS , 2002 .

[21]  Vassili Toropov,et al.  Modelling and Approximation Strategies in Optimization — Global and Mid-Range Approximations, Response Surface Methods, Genetic Programming, Low / High Fidelity Models , 2001 .

[22]  C. Fleury First and second order convex approximation strategies in structural optimization , 1989 .

[23]  Raphael T. Haftka,et al.  Sensitivity-based scaling for approximating. Structural response , 1993 .

[24]  G. Gary Wang,et al.  Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .

[25]  Li Liu,et al.  A Sequential Maximin Latin Hypercube Sampling Method And Its Application to Aircraft Design , 2015 .

[26]  Richard P. Dwight,et al.  Exploiting Adjoint Derivatives in High-Dimensional Metamodels , 2015 .

[27]  D. Dennis,et al.  SDO : A Statistical Method for Global Optimization , 1997 .

[28]  John E. Renaud,et al.  Concurrent Subspace Optimization Using Design Variable Sharing in a Distributed Computing Environment , 1996 .

[29]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[30]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[31]  R. Tibshirani,et al.  Combining Estimates in Regression and Classification , 1996 .

[32]  Raphael T. Haftka,et al.  Variable complexity design of composite fuselage frames by response surface techniques 1 This articl , 1998 .

[33]  Jack P. C. Kleijnen,et al.  Application-driven sequential designs for simulation experiments: Kriging metamodelling , 2004, J. Oper. Res. Soc..

[34]  Y. Selen,et al.  Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[35]  Donald R. Jones,et al.  Global optimization of deceptive functions with sparse sampling , 2008 .

[36]  Timothy W. Simpson,et al.  Trade Space Exploration of a Wing Design Problem Using Visual Steering and Multi-Dimensional Data Visualization , 2008 .

[37]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[38]  Noel A. C. Cressie,et al.  Statistics for Spatial Data: Cressie/Statistics , 1993 .

[39]  Mary Frecker,et al.  Metamodel-Driven Interfaces for Engineering Design: Impact of Delay and Problem Size on User Performance , 2005 .

[40]  T. J. Mitchell,et al.  Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments , 1991 .

[41]  Timothy W. Simpson,et al.  Metamodel-Driven Design Optimization Using Integrative Graphical Design Interfaces: Results From a Job-Shop Manufacturing Simulation Experiment , 2005, J. Comput. Inf. Sci. Eng..

[42]  J. Yamamoto An Alternative Measure of the Reliability of Ordinary Kriging Estimates , 2000 .

[43]  Bernard Grossman,et al.  Noisy Aerodynamic Response and Smooth Approximations in HSCT Design , 1994 .

[44]  Abdus Samad,et al.  Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion , 2015 .

[45]  J. Barthelemy,et al.  Two point exponential approximation method for structural optimization , 1990 .

[46]  Eliot Winer,et al.  Visual design steering for optimization solution improvement , 2000 .

[47]  Kemper Lewis,et al.  Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space , 2004 .

[48]  Bryan Glaz,et al.  Multiple-Surrogate Approach to Helicopter Rotor Blade Vibration Reduction , 2009 .

[49]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[50]  Raphael T. Haftka,et al.  Design of Shell Structures for Buckling Using Correction Response Surface Approximations , 1998 .

[51]  Joaquim R. R. A. Martins,et al.  Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis , 2015 .

[52]  Raphael T. Haftka,et al.  CORRECTION RESPONSE SURFACE APPROXIMATIONS FOR STRESS INTENSITY FACTORS OF A COMPOSITE STIFFENED PLATE , 1998 .

[53]  T. Simpson,et al.  Computationally Inexpensive Metamodel Assessment Strategies , 2002 .

[54]  Michael S. Eldred,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual. , 2010 .

[55]  Weiyu Liu,et al.  Gradient-Enhanced Response Surface Approximations Using Kriging Models , 2002 .

[56]  Pauli Pedersen,et al.  The Integrated Approach of FEM-SLP for Solving Problems of Optimal Design , 1981 .

[57]  Liping Wang,et al.  Improving High-Dimensional Physics Models Through Bayesian Calibration With Uncertain Data , 2012 .

[58]  Abdus Samad,et al.  Jet Pump Design Optimization by Multi-Surrogate Modeling , 2015 .

[59]  Richard P. Dwight,et al.  Uncertainty quantification for a sailing yacht hull, using multi-fidelity kriging , 2015 .

[60]  J. Alonso,et al.  Using gradients to construct cokriging approximation models for high-dimensional design optimization problems , 2002 .

[61]  Jay D. Martin,et al.  A Methodology to Manage System-level Uncertainty During Conceptual Design , 2006 .

[62]  Fred van Keulen,et al.  Gradient-enhanced response surface building , 2002 .

[63]  Agus Sudjianto,et al.  Blind Kriging: A New Method for Developing Metamodels , 2008 .

[64]  R. Lewis,et al.  An Overview of First-Order Model Management for Engineering Optimization , 2001 .

[65]  Eliot Winer,et al.  Development of visual design steering as an aid in large-scale multidisciplinary design optimization. Part I: method development , 2002 .

[66]  Daniel M. Dunlavy,et al.  Formulations for Surrogate-Based Optimization with Data Fit, Multifidelity, and Reduced-Order Models , 2006 .

[67]  R. Haftka,et al.  Multiple Surrogates for the Shape Optimization of Bluff Body-Facilitated Mixing , 2005 .

[68]  J. F. Rodríguez,et al.  Sequential approximate optimization using variable fidelity response surface approximations , 2000 .

[69]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[70]  Greg F. Naterer,et al.  Extended Collaboration Pursuing Method for Solving Larger Multidisciplinary Design Optimization Problems , 2007 .

[71]  G. G. Wang,et al.  Mode Pursuing Sampling Method for Discrete Variable Optimization on Expensive Black-Box Functions , 2008 .

[72]  Timothy W. Simpson,et al.  Sampling Strategies for Computer Experiments , 2001 .

[73]  Daniel C. Kammer,et al.  Component metamodel synthesis for the construction of master response surfaces , 2001 .

[74]  Ping Zhang Model Selection Via Multifold Cross Validation , 1993 .

[75]  Salvador A. Pintos,et al.  Toward an optimal ensemble of kernel-based approximations with engineering applications , 2006 .

[76]  Andy J. Keane,et al.  Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods , 2003 .

[77]  Vassili Toropov,et al.  Metamodel-based collaborative optimization framework , 2009 .

[78]  John W. Bandler,et al.  Quality assessment of coarse models and surrogates for space mapping optimization , 2008 .

[79]  Christian Noon,et al.  Immersive Product Configurator for Conceptual Design , 2007, DAC 2007.

[80]  Michael S. Eldred,et al.  DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Developers Manual (title change from electronic posting) , 2002 .

[81]  P. Villon,et al.  Moving least squares response surface approximation: Formulation and metal forming applications , 2005 .

[82]  L. Schmit,et al.  Some Approximation Concepts for Structural Synthesis , 1974 .

[83]  Art B. Owen,et al.  9 Computer experiments , 1996, Design and analysis of experiments.

[84]  J.W. Bandler,et al.  Space mapping: the state of the art , 2004, IEEE Transactions on Microwave Theory and Techniques.

[85]  Dimitri N. Mavris,et al.  Multi-Source Surrogate Modeling with Bayesian Hierarchical Regression , 2015 .

[86]  Garret N. Vanderplaats,et al.  VisualDOC: A Software System for General Purpose Integration and Design Optimization , 2002 .

[87]  Anirban Chaudhuri,et al.  Efficient Global Optimization with Adaptive Target for Probability of Targeted Improvement , 2012 .

[88]  T. Simpson A concept exploration method for product family design , 1998 .

[89]  Filip De Turck,et al.  Blind Kriging: Implementation and performance analysis , 2012, Adv. Eng. Softw..

[90]  T Watson Layne,et al.  Multidisciplinary Optimization of a Supersonic Transport Using Design of Experiments Theory and Response Surface Modeling , 1997 .

[91]  Vassili Toropov,et al.  Optimum blank design for sheet metal forming based on the interaction of high- and low-fidelity FE models , 2006 .

[92]  Changha Hwang,et al.  PREDICTION INTERVALS FOR SUPPORT VECTOR MACHINE REGRESSION , 2002 .

[93]  Farrokh Mistree,et al.  Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size , 1999 .

[94]  Michael S. Eldred,et al.  OVERVIEW OF MODERN DESIGN OF EXPERIMENTS METHODS FOR COMPUTATIONAL SIMULATIONS , 2003 .

[95]  V. Braibant,et al.  Structural optimization: A new dual method using mixed variables , 1986 .

[96]  Joaquim R. R. A. Martins,et al.  Surrogate models and mixtures of experts in aerodynamic performance prediction for mission analysis , 2014 .

[97]  William H. Mason,et al.  Variable-complexity aerodynamic-structural design of a high-speed civil transport wing , 1992 .

[98]  Abdus Samad,et al.  Surrogate Assisted Design Optimization of an Air Turbine , 2014 .

[99]  J. P. Evans,et al.  Interdigitation for effective design space exploration using iSIGHT , 2002 .

[100]  John W. Bandler,et al.  An Introduction to the Space Mapping Technique , 2001 .

[101]  Peter Hollingsworth,et al.  Gaussian Process Meta-modeling: Comparison of Gausian Process Training Methods , 2003 .

[102]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[103]  Layne T. Watson,et al.  Dependence of optimal structural weight on aerodynamic shape for a High Speed Civil Transport , 1996 .

[104]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[105]  Vassili Toropov,et al.  Simulation approach to structural optimization , 1989 .

[106]  Raphael T. Haftka,et al.  Variable-complexity aerodynamic optimization of a high-speed civil transport wing , 1994 .

[107]  S. Koziel,et al.  A Space-Mapping Framework for Engineering Optimization—Theory and Implementation , 2006, IEEE Transactions on Microwave Theory and Techniques.

[108]  Raphael T. Haftka,et al.  Optimization and Experiments: A Survey , 1998 .

[109]  Kwang-Yong Kim,et al.  Multiple surrogate modeling for axial compressor blade shape optimization , 2008 .

[110]  Anthony A. Giunta,et al.  Aircraft Multidisciplinary Design Optimization using Design of Experiments Theory and Response Surface Modeling Methods , 1997 .

[111]  S. Rippa,et al.  Numerical Procedures for Surface Fitting of Scattered Data by Radial Functions , 1986 .

[112]  Wei Chen,et al.  A CONCEPT EXPLORATION METHOD FOR DETERMINING ROBUST TOP-LEVEL SPECIFICATIONS , 1996 .

[113]  C. Gearhart,et al.  Bayesian metrics for comparing response surface models of data with uncertainty , 2001 .

[114]  Thomas J. Goodman,et al.  The effect of System Response Time on interactive computer aided problem solving , 1978, SIGGRAPH.

[115]  Derek J. Pike,et al.  Empirical Model‐building and Response Surfaces. , 1988 .

[116]  Vassili Toropov,et al.  Multiparameter structural optimization using FEM and multipoint explicit approximations , 1993 .

[117]  R. Haftka,et al.  Multiple surrogates: how cross-validation errors can help us to obtain the best predictor , 2009 .

[118]  Ren-Jye Yang,et al.  Approximation methods in multidisciplinary analysis and optimization: a panel discussion , 2004 .

[119]  Ruichen Jin,et al.  On Sequential Sampling for Global Metamodeling in Engineering Design , 2002, DAC 2002.

[120]  Don Beeson,et al.  Valuable Theoretical Lessons Learned From the Application of Metamodels to a Variety of Industrial Problems , 2009, DAC 2009.

[121]  Rahul Rai,et al.  Q2S2: A New Methodology for Merging Quantitative and Qualitative Information in Experimental Design , 2008 .

[122]  Prabhat Hajela,et al.  Neural networks in structural analysis and design - An overview , 1992 .

[123]  Raphael T. Haftka,et al.  Using Multiple Surrogates for Minimization of the RMS Error in Meta-Modeling , 2008, DAC 2008.

[124]  N. Tzannetakis,et al.  Design optimization through parallel-generated surrogate models, optimization methodologies and the utility of legacy simulation software , 2002 .

[125]  Cori L. Ignatovich,et al.  Physical Surrogates in Design Optimization for Enhanced Crashworthiness , 2002 .

[126]  J. S. Hunter,et al.  Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. , 1979 .

[127]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[128]  J. Arbocz,et al.  Multi-fidelity optimization of laminated conical shells for buckling , 2005 .

[129]  Andy J. Keane,et al.  A Constraint Mapping Approach to the Structural Optimization of an Expensive Model using Surrogates , 2001 .

[130]  John Rasmussen Accumulated approximation: A new method for structural optimization by iterative improvement , 1990 .

[131]  Dimitri N. Mavris,et al.  Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design , 1999 .

[132]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[133]  L. Watson,et al.  Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization , 1998 .

[134]  G. G. Wang,et al.  Metamodeling for High Dimensional Simulation-Based Design Problems , 2010 .

[135]  Vassili Toropov,et al.  Parameter Identification for Nonlinear Constitutive Models: Finite Element Simulation — Optimization — Nontrivial Experiments , 1993 .

[136]  Nestor V. Queipo,et al.  Adaptive Reduction of Design Variables Using Global Sensitivity in Reliability-Based Optimization , 2004 .

[137]  J. Friedman Multivariate adaptive regression splines , 1990 .

[138]  Timothy W. Simpson,et al.  Sampling Strategies for Computer Experiments: Design and Analysis , 2001 .

[139]  Farrokh Mistree,et al.  Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .

[140]  Ning Sun,et al.  Study on Finite Element Model of Pneumatic Artificial Muscle , 2012 .

[141]  Kemper Lewis,et al.  Visualization of multidimensional design and optimization data using cloud visualization , 2002, DAC 2002.

[142]  John W. Bandler,et al.  A trust region aggressive space mapping algorithm for EM optimization , 1998, IMS 1998.

[143]  Timothy W. Simpson,et al.  Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example , 2009, J. Comput. Inf. Sci. Eng..

[144]  Zheng Li,et al.  Global Optimization Based on Weighting-Integral Expected Improvement , 2012 .

[145]  Raphael T. Haftka,et al.  Response surface approximations for structural optimization , 1996 .

[146]  B. Grossman,et al.  Variable-complexity response surface approximations for wing structural weight in HSCT design , 1996 .

[147]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[148]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[149]  Bernard Grossman,et al.  Variable Complexity Response Surface Design of an HSCT Con guration , 2003 .

[150]  Jack P. C. Kleijnen,et al.  A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation” , 1975 .

[151]  Vassili Toropov,et al.  MULTILEVEL OPTIMIZATION OF THE DYNAMIC BEHAVIOUR OF A LINEAR MECHANICAL SYSTEM WITH MULTIPOINT APPROXIMATION , 1996 .

[152]  李幼升,et al.  Ph , 1989 .

[153]  G. G. Wang,et al.  Mode-pursuing sampling method for global optimization on expensive black-box functions , 2004 .

[154]  Ren-Jye Yang,et al.  Design for six sigma through robust optimization , 2004 .

[155]  R. Rikards,et al.  Elaboration of Optimal Design Models for Objects from Data of Experiments , 1993 .

[156]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[157]  Efrain Nava,et al.  Setting targets for surrogate-based optimization , 2010, J. Glob. Optim..

[158]  Kemper Lewis,et al.  A Multidimensional Visualization Interface to Aid in Trade-off Decisions During the Solution of Coupled Subsystems Under Uncertainty , 2006, J. Comput. Inf. Sci. Eng..

[159]  Valeri Markine Optimization of the Dynamic Behaviour of Mechanical Systems , 1999 .

[160]  Andy J. Keane,et al.  A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization , 2003, J. Glob. Optim..

[161]  van Dh Dick Campen,et al.  Optimization of Multibody Systems Using Approximation Concepts , 1996 .

[162]  N. M. Alexandrov,et al.  A trust-region framework for managing the use of approximation models in optimization , 1997 .

[163]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[164]  Wang Hu,et al.  Optimization of sheet metal forming processes by the use of space mapping based metamodeling method , 2008 .

[165]  J. -F. M. Barthelemy,et al.  Approximation concepts for optimum structural design — a review , 1993 .

[166]  Serhat Yesilyurt,et al.  Bayesian-validated computer-simulation surrogates for optimization and design: error estimates and applications , 1997 .

[167]  Nestor V. Queipo,et al.  Adaptive reduction of random variables using global sensitivity in reliability-based optimisation , 2006 .

[168]  Larry Bull,et al.  Design Mining Interacting Wind Turbines , 2014, Evolutionary Computation.

[169]  R. Haftka,et al.  Ensemble of surrogates , 2007 .

[170]  G. Gary Wang,et al.  Collaboration Pursuing Method for MDO Problems , 2005 .

[171]  Michael James Sasena,et al.  Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. , 2002 .

[172]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[173]  A. O'Hagan,et al.  Predicting the output from a complex computer code when fast approximations are available , 2000 .

[174]  Raphael T. Haftka,et al.  Structural optimization complexity: what has Moore’s law done for us? , 2004 .

[175]  T. Simpson,et al.  Comparative studies of metamodeling techniques under multiple modeling criteria , 2000 .

[176]  Ajg Bert Schoofs Experimental Design and Structural Optimization , 1988 .

[177]  Marcus Redhe,et al.  A multipoint version of space mapping optimization applied to vehicle crashworthiness design , 2006 .

[178]  Slawomir Koziel,et al.  Surrogate modelling and optimization using shape-preserving response prediction: A review , 2016 .

[179]  Vassili Toropov,et al.  Multipoint Approximation Method for Structural Optimization Problems with Noisy Function Values , 1995 .

[180]  Claude Fleury,et al.  CONLIN: An efficient dual optimizer based on convex approximation concepts , 1989 .

[181]  Juan J. Alonso,et al.  Design of a Low-Boom Supersonic Business Jet Using Cokriging Approximation Models , 2002 .

[182]  Salvador Pintos,et al.  Global sensitivity analysis of Alkali–Surfactant–Polymer enhanced oil recovery processes , 2007 .

[183]  Theresa Dawn Robinson,et al.  Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping , 2008 .

[184]  Raphael T. Haftka,et al.  Assessing the value of another cycle in Gaussian process surrogate-based optimization , 2009 .

[185]  Sophia Lefantzi,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. , 2011 .

[186]  M. Araújo,et al.  The importance of biotic interactions for modelling species distributions under climate change , 2007 .

[187]  K. Choi,et al.  Efficient Response Surface Modeling by Using Moving Least-Squares Method and Sensitivity , 2005 .

[188]  Bernard Grossman,et al.  Variable-complexity response surface aerodynamic design of an HSCT wing , 1995 .

[189]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[190]  Luc Pronzato,et al.  Design of computer experiments: space filling and beyond , 2011, Statistics and Computing.

[191]  Hu Wang,et al.  Adaptive MLS-HDMR metamodeling techniques for high dimensional problems , 2011, Expert Syst. Appl..

[192]  Masoud Rais-Rohani,et al.  Ensemble of Metamodels with Optimized Weight Factors , 2008 .

[193]  Layne T. Watson,et al.  Two-point constraint approximation in structural optimization , 1987 .

[194]  Michael S. Eldred,et al.  Optimization Under Uncertainty Methods for Computational Shock Physics Applications , 2002 .

[195]  Efrain Nava,et al.  A Geostatistical Perspective for the Surrogate-Based Integration of Variable Fidelity Models , 2010 .

[196]  J. Dennis,et al.  MANAGING APPROXIMATION MODELS IN OPTIMIZATION , 2007 .

[197]  Felipe A. C. Viana,et al.  A Tutorial on Latin Hypercube Design of Experiments , 2016, Qual. Reliab. Eng. Int..

[198]  Daniel Friedrich,et al.  Multi-objective optimisation using surrogate models for the design of VPSA systems , 2015, Comput. Chem. Eng..

[199]  P. Bouillard,et al.  Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion , 2011 .

[200]  Julien Bect,et al.  Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion , 2011, LION.

[201]  Farrokh Mistree,et al.  Facilitating Concept Exploration for Configuring Turbine Propulsion Systems , 1998 .

[202]  Mary Frecker,et al.  Impact of response delay and training on user performance with text-based and graphical user interfaces for engineering design , 2007 .

[203]  Nilay Shah,et al.  Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation , 2009, 2009 First International Conference on Advances in System Simulation.

[204]  Alexander I. J. Forrester,et al.  Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[205]  Filip De Turck,et al.  Evolutionary Model Type Selection for Global Surrogate Modeling , 2009, J. Mach. Learn. Res..

[206]  Salvador Pintos,et al.  An Optimization Methodology of Alkaline-Surfactant-Polymer Flooding Processes Using Field Scale Numerical Simulation and Multiple Surrogates , 2004 .

[207]  Vladimir Balabanov,et al.  Multi-Fidelity Optimization with High-Fidelity Analysis and Low-Fidelity Gradients , 2004 .

[208]  G. N. Vanderplaats,et al.  Effective use of numerical optimization in structural design , 1989 .

[209]  David J. J. Toal,et al.  A study into the potential of GPUs for the efficient construction and evaluation of Kriging models , 2015, Engineering with Computers.

[210]  Bernard Grossman,et al.  Response Surface Models Combining Linear and Euler Aerodynamics for Supersonic Transport Design , 1999 .

[211]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[212]  L. A. Schmit,et al.  Structural synthesis - Its genesis and development , 1981 .

[213]  Hui Zhou,et al.  An adaptive global variable fidelity metamodeling strategy using a support vector regression based scaling function , 2015, Simul. Model. Pract. Theory.

[214]  D. Dennis,et al.  A statistical method for global optimization , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

[215]  K. Svanberg The method of moving asymptotes—a new method for structural optimization , 1987 .

[216]  R. Haftka,et al.  Estimating training data boundaries in surrogate-based modeling , 2010 .

[217]  S. Rahman Reliability Engineering and System Safety , 2011 .

[218]  Runze Li,et al.  Design and Modeling for Computer Experiments , 2005 .

[219]  Mikael A. Langthjem,et al.  Multifidelity Response Surface Approximations for the Optimum Design of Diffuser Flows , 2001 .

[220]  David J. J. Toal,et al.  Kriging Hyperparameter Tuning Strategies , 2008 .

[221]  C. Bloebaum,et al.  Intuitive Visualization of Hyperspace Pareto Frontier for Robustness in Multi-Attribute Decision-Making , 2006 .

[222]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[223]  Xin Chen,et al.  A deterministic sequential maximin Latin hypercube design method using successive local enumeration for metamodel-based optimization , 2016 .

[224]  Shawn E. Gano,et al.  Hybrid Variable Fidelity Optimization by Using a Kriging-Based Scaling Function , 2005 .

[225]  Ren-Jye Yang,et al.  High Performance Computing and Surrogate Modeling for Rapid Visualization with Multidisciplinary Optimization , 2004 .

[226]  Achille Messac,et al.  Metamodeling using extended radial basis functions: a comparative approach , 2006, Engineering with Computers.

[227]  J. Utans,et al.  Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction , 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

[228]  R. Haftka,et al.  Importing Uncertainty Estimates from One Surrogate to Another , 2009 .

[229]  R. L. Hardy Multiquadric equations of topography and other irregular surfaces , 1971 .

[230]  John E. Renaud,et al.  Variable Fidelity Optimization Using a Kriging Based Scaling Function , 2004 .

[231]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[232]  R. Haftka Combining global and local approximations , 1991 .

[233]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[234]  Jack P. C. Kleijnen,et al.  The correct Kriging variance estimated by bootstrapping , 2006, J. Oper. Res. Soc..

[235]  Yao Lin,et al.  An Efficient Robust Concept Exploration Method and Sequential Exploratory Experimental Design , 2004 .

[236]  Robert Hewson,et al.  Metamodelling Based on High and Low Fidelity Models Interaction for UAV Gust Performance Optimization , 2009 .

[237]  Ellen B. Roecker,et al.  Prediction error and its estimation for subset-selected models , 1991 .

[238]  Raphael T. Haftka,et al.  Multi-Fidelity Analysis of Corrugated-Core Sandwich Panels for Integrated Thermal Protection Systems , 2009 .