Managing computational complexity using surrogate models: a critical review

[1]  Charlie C. L. Wang,et al.  Space-time topology optimization for additive manufacturing , 2020 .

[2]  Farrokh Mistree,et al.  Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets , 2019, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[3]  Mohammed Reza Kianifar,et al.  Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide , 2019, Structural and Multidisciplinary Optimization.

[4]  Yu Wang,et al.  Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater , 2019, Environmental Engineering Science.

[5]  Yi Ji,et al.  Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model , 2019, Journal of Hydrology.

[6]  Muqdad Al-Juboori,et al.  Reliability-based optimum design of hydraulic water retaining structure constructed on heterogeneous porous media: utilizing stochastic ensemble surrogate model-based linked simulation optimization model , 2019, Life Cycle Reliability and Safety Engineering.

[7]  T. Cai,et al.  Semi‐supervised validation of multiple surrogate outcomes with application to electronic medical records phenotyping , 2019, Biometrics.

[8]  Peng Luo,et al.  Criteria for Multiple Surrogates , 2019, Statistica Sinica.

[9]  R. L. Riche,et al.  An Overview of Gradient-Enhanced Metamodels with Applications , 2017, Archives of Computational Methods in Engineering.

[10]  Quanli Liu,et al.  A multiple surrogates based PSO algorithm , 2019, Artificial Intelligence Review.

[11]  John G. Michopoulos,et al.  Enriched analytical solutions for additive manufacturing modeling and simulation , 2018, Additive Manufacturing.

[12]  Benjamin Peherstorfer,et al.  Survey of multifidelity methods in uncertainty propagation, inference, and optimization , 2018, SIAM Rev..

[13]  Tapabrata Ray,et al.  Multiple Surrogate-Assisted Many-Objective Optimization for Computationally Expensive Engineering Design , 2018 .

[14]  Jie Zhang,et al.  An Advanced and Robust Ensemble Surrogate Model: Extended Adaptive Hybrid Functions , 2018 .

[15]  Doreen Ying Ying Sim,et al.  Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea , 2018 .

[16]  Pengcheng Ye,et al.  Ensemble of surrogate based global optimization methods using hierarchical design space reduction , 2018 .

[17]  Hongbing Fang,et al.  On the ensemble of metamodels with multiple regional optimized weight factors , 2018 .

[18]  Huanhuan Gao,et al.  Categorical structural optimization using discrete manifold learning approach and custom-built evolutionary operators , 2018 .

[19]  Abdus Samad,et al.  Shape optimization of a bidirectional impulse turbine via surrogate models , 2018 .

[20]  Marco Montemurro,et al.  A surrogate model based on Non-Uniform Rational B-Splines hypersurfaces , 2018 .

[21]  Ivo D. Dinov,et al.  Deep learning for neural networks , 2018 .

[22]  Hema R. Madala,et al.  Inductive Learning Algorithms for Complex Systems Modeling , 2017 .

[23]  Tapabrata Ray,et al.  A multiple surrogate assisted evolutionary algorithm for optimization involving iterative solvers , 2017 .

[24]  Tanmoy Mukhopadhyay,et al.  Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment , 2017 .

[25]  Nicolas Huck,et al.  Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , 2017, Eur. J. Oper. Res..

[26]  Kalyanmoy Deb,et al.  Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results , 2017, EMO.

[27]  Xin Yao,et al.  Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results , 2017, EMO.

[28]  Jianguang Fang,et al.  On design optimization for structural crashworthiness and its state of the art , 2017 .

[29]  Tapabrata Ray,et al.  Multi-Objective Optimization Using an Evolutionary Algorithm Embedded with Multiple Spatially Distributed Surrogates , 2017 .

[30]  Abdus Samad,et al.  An alternative approach to surrogate averaging for a centrifugal impeller shape optimisation , 2017, Int. J. Comput. Aided Eng. Technol..

[31]  Xiaoping Du,et al.  Reliability Analysis With Monte Carlo Simulation and Dependent Kriging Predictions , 2016 .

[32]  Gill Bejerano,et al.  M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity , 2016, Nature Genetics.

[33]  Tapabrata Ray,et al.  Multi-Objective Optimization With Multiple Spatially Distributed Surrogates , 2016 .

[34]  Enying Li,et al.  A comparative study of expected improvement-assisted global optimization with different surrogates , 2016 .

[35]  Wei Chen,et al.  Reduction of Epistemic Model Uncertainty in Simulation-Based Multidisciplinary Design , 2016 .

[36]  Kalyanmoy Deb,et al.  Breaking the Billion-Variable Barrier in Real-World Optimization Using a Customized Evolutionary Algorithm , 2016, GECCO.

[37]  Jian Liu,et al.  An efficient ensemble of radial basis functions method based on quadratic programming , 2016 .

[38]  Heeyoung Kim,et al.  A new metric of absolute percentage error for intermittent demand forecasts , 2016 .

[39]  Tapabrata Ray,et al.  Multiple surrogate assisted multiobjective optimization using improved pre-selection , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[40]  De La Fuente,et al.  Simulation Metamodeling with Gaussian Process: A Numerical Study. , 2016 .

[41]  R. Alizadeh,et al.  A Combined Model of Scenario Planning and Assumption-Based Planning For Futurology, and Robust Decision Making in the Energy Sector , 2016 .

[42]  Indranil Pan,et al.  Performance comparison of several response surface surrogate models and ensemble methods for water injection optimization under uncertainty , 2016, Comput. Geosci..

[43]  Anirban Chaudhuri,et al.  Parallel surrogate-assisted global optimization with expensive functions – a survey , 2016 .

[44]  Reza Maknoon,et al.  An integrated scenario-based robust planning approach for foresight and strategic management with application to energy industry , 2016 .

[45]  Sankaran Mahadevan,et al.  Stochastic Multidisciplinary Analysis with High-Dimensional Coupling , 2016 .

[46]  B. Chon,et al.  Application of computational fluid dynamics and surrogate-coupled evolutionary computing to enhance centrifugal-pump performance , 2016 .

[47]  Simon R. Goerger,et al.  Integrating External Simulations Within the Model-Based Systems Engineering Approach Using Statistical Metamodels , 2016 .

[48]  Cameron J. Turner,et al.  Inverse characterization of composite materials via surrogate modeling , 2015 .

[49]  Cameron J. Turner,et al.  Applying NURBs-Based Surrogate Models for Performance Forecasting in Manufacturing Systems , 2015 .

[50]  Cameron J. Turner,et al.  Graph analysis of non-uniform rational B-spline-based metamodels , 2015 .

[51]  Cameron J. Turner,et al.  Pseudo Elimination of Geometry Dependence in Surrogate Models of Distributed Knee Loads From an Explicit Dynamic Finite Element Analysis , 2015 .

[52]  John G. Michopoulos,et al.  Towards Real-Time Composite Material Characterization Using Surrogate Models and GPGPU Computing , 2015 .

[53]  Duane Detwiler,et al.  Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates , 2015 .

[54]  Erdem Acar,et al.  Effect of error metrics on optimum weight factor selection for ensemble of metamodels , 2015, Expert Syst. Appl..

[55]  Kalyanmoy Deb,et al.  A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results , 2015, EMO.

[56]  Abdus Samad,et al.  Design Optimization of Electric Centrifugal Pump by Multiple Surrogate Models , 2015 .

[57]  Randy R. Sitter,et al.  Using Genetic Algorithms to Design Experiments: A Review , 2015, Qual. Reliab. Eng. Int..

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

[59]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[60]  K. Choi,et al.  An efficient variable screening method for effective surrogate models for reliability-based design optimization , 2014 .

[61]  Wei Gong,et al.  An evaluation of adaptive surrogate modeling based optimization with two benchmark problems , 2014, Environ. Model. Softw..

[62]  J. Steuben,et al.  Adaptive Surrogate-Model Fitting Using Error Monotonicity , 2014 .

[63]  R. Haftka,et al.  Efficient Global Optimization with Adaptive Target Setting , 2014 .

[64]  Timothy W. Simpson,et al.  Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? , 2014 .

[65]  Ramana V. Grandhi,et al.  A survey of structural and multidisciplinary continuum topology optimization: post 2000 , 2014 .

[66]  John C. Steuben Massively parallel engineering simulations on graphics processors: Parallelization, synchronization, and approximation , 2014 .

[67]  Richard H. Crawford,et al.  Robust engineering design optimization with non-uniform rational B-splines-based metamodels , 2013 .

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

[69]  Shifeng Xiong,et al.  Sequential Design and Analysis of High-Accuracy and Low-Accuracy Computer Codes , 2013, Technometrics.

[70]  Lan Wang,et al.  Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data , 2013, 1304.2186.

[71]  Per Christian Hansen,et al.  Least Squares Data Fitting with Applications , 2012 .

[72]  Cameron J. Turner,et al.  Robust Optimization of Mixed-Integer Problems Using NURBs-Based Metamodels , 2012, J. Comput. Inf. Sci. Eng..

[73]  Anirban Basudhar,et al.  Selection of anisotropic kernel parameters using multiple surrogate information , 2012 .

[74]  Witold Pedrycz,et al.  Effective Noise Estimation-Based Online Prediction for Byproduct Gas System in Steel Industry , 2012, IEEE Transactions on Industrial Informatics.

[75]  Carlos A. Coello Coello,et al.  Multi-objective airfoil shape optimization using a multiple-surrogate approach , 2012, 2012 IEEE Congress on Evolutionary Computation.

[76]  Wenze Shao,et al.  Bayesian Metamodeling for Computer Experiments Using the Gaussian Kriging Models , 2012, Qual. Reliab. Eng. Int..

[77]  Andrea Castelletti,et al.  Emulation techniques for the reduction and sensitivity analysis of complex environmental models , 2012, Environ. Model. Softw..

[78]  Marine Lacoste,et al.  Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach , 2012 .

[79]  Yizhong Ma,et al.  A Bayesian Approach to Kriging Metamodeling for Computer Experiments , 2012 .

[80]  J. Morlier,et al.  Surrogate modeling approximation using a mixture of experts based on EM joint estimation , 2011 .

[81]  André I. Khuri,et al.  Response surface methodology , 2010 .

[82]  Cameron J. Turner,et al.  Waypoint-Based Robot Navigation Using NURBs-Based Metamodels , 2011 .

[83]  Cameron J. Turner,et al.  A Review and Evaluation of Existing Adaptive Sampling Criteria and Methods for the Creation of NURBs-Based Metamodels , 2011 .

[84]  Cameron J. Turner,et al.  Metamodel-Assisted Ice Detection for Wind Turbine Blades , 2011 .

[85]  Cameron J. Turner,et al.  Robust Optimization and Analysis of NURBs-Based Metamodels Using Graph Theory , 2011 .

[86]  Cameron J. Turner Metamodeling in Product and Process Design , 2011 .

[87]  Farrokh Mistree,et al.  Model Selection Under Limited Information Using a Value-of-Information-Based Indicator , 2010 .

[88]  Victor Picheny,et al.  Using Cross Validation to Design Conservative Surrogates , 2010 .

[89]  Victor Picheny,et al.  Adaptive Designs of Experiments for Accurate Approximation of a Target Region , 2010 .

[90]  Afzal Suleman,et al.  Comparison of Surrogate Models in a Multidisciplinary Optimization Framework for Wing Design , 2010 .

[91]  Raphael T. Haftka,et al.  Control-Oriented Design Using H-infinity Synthesis and Multiple Surrogates , 2010 .

[92]  L. Watson,et al.  Why Not Run the Efficient Global Optimization Algorithm with Multiple Surrogates , 2010 .

[93]  Wynne W. Chin,et al.  Handbook of Partial Least Squares , 2010 .

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

[95]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[96]  Giovanni Seni,et al.  Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions , 2010, Ensemble Methods in Data Mining.

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

[98]  Cameron J. Turner,et al.  Robust Optimization Exploration Using NURBs-Based Metamodeling Techniques , 2010 .

[99]  Cameron J. Turner Diagnosis via NURBs Metamodel , 2010 .

[100]  Cameron J. Turner,et al.  Data Modeling Using NURBs Curves and Modified Genetic Algorithms , 2010 .

[101]  Raphael T. Haftka,et al.  Making the Most Out of Surrogate Models: Tricks of the Trade , 2010, DAC 2010.

[102]  Peter Z. G. Qian Nested Latin hypercube designs , 2009 .

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

[104]  Peter Z. G. Qian,et al.  CONSTRUCTION OF NESTED SPACE-FILLING DESIGNS , 2009, 0909.0598.

[105]  Richard H. Crawford,et al.  N -Dimensional Nonuniform Rational B-Splines for Metamodeling , 2009, J. Comput. Inf. Sci. Eng..

[106]  Robert L. Mason,et al.  Fractional factorial design , 2009 .

[107]  Frederick Kin Hing Phoa,et al.  Analysis of Supersaturated Designs via Dantzig Selector , 2009 .

[108]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[109]  Christiaan J. J. Paredis,et al.  Managing Design-Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling , 2009, J. Comput. Inf. Sci. Eng..

[110]  Victor Picheny,et al.  Conservative Prediction via Safety Margin: Design Through Cross-Validation and Benefits of Multiple Surrogates , 2009, DAC 2009.

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

[112]  Boxin Tang,et al.  NESTED SPACE-FILLING DESIGNS FOR COMPUTER EXPERIMENTS WITH TWO LEVELS OF ACCURACY , 2009 .

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

[114]  Timothy W. Simpson,et al.  Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not , 2008 .

[115]  Peter Z. G. Qian,et al.  Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors , 2008, Technometrics.

[116]  Wei Chen,et al.  A better understanding of model updating strategies in validating engineering models , 2008 .

[117]  Stefan M. Wild,et al.  Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using Optimization and Radial Basis Function Approximation , 2008 .

[118]  John W. Bandler,et al.  Editorial—surrogate modeling and space mapping for engineering optimization , 2008 .

[119]  Christiaan J. J. Paredis,et al.  A value-of-information based approach to simulation model refinement , 2008 .

[120]  Richard H. Crawford,et al.  Robust optimization with NURBs hypermodels , 2008 .

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

[122]  Juan M. Corchado,et al.  Innovations in Hybrid Intelligent Systems , 2008, Advances in Soft Computing.

[123]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[124]  Juan M. Corchado,et al.  Innovations in Hybrid Intelligent Systems (Advances in Soft Computing) , 2007 .

[125]  Christine A. Shoemaker,et al.  A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions , 2007, INFORMS J. Comput..

[126]  Bernhard Sendhoff,et al.  A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation , 2007, GECCO '07.

[127]  Richard H. Crawford,et al.  Global optimization of NURBs-based metamodels , 2007 .

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

[129]  G. De’ath Boosted trees for ecological modeling and prediction. , 2007, Ecology.

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

[131]  C. Shoemaker,et al.  Assessing the impacts of parameter uncertainty for computationally expensive groundwater models , 2006 .

[132]  Laura Painton Swiler,et al.  Calibration, validation, and sensitivity analysis: What's what , 2006, Reliab. Eng. Syst. Saf..

[133]  Raphael T. Haftka,et al.  Performance Estimate and Simultaneous Application of Multiple Surrogates , 2006 .

[134]  Roger G. Ghanem,et al.  On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data , 2006, J. Comput. Phys..

[135]  Taho Yang,et al.  Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study , 2006 .

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

[137]  Siow-Yong Lim,et al.  Reply to comment by M. Bayani Cardenas and John L. Wilson on “Flow resistance and bed form geometry in a wide alluvial channel” , 2006 .

[138]  Russell R. Barton,et al.  A review on design, modeling and applications of computer experiments , 2006 .

[139]  Richard H. Crawford,et al.  Fault detection with nurbs-based metamodels , 2006 .

[140]  Kwang-Yong Kim,et al.  Shape optimization of turbomachinery blade using multiple surrogate models , 2006 .

[141]  Carolyn Pillers Dobler,et al.  Brief Reviews of Teaching Materials , 2005 .

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

[143]  E. M. Kleinberg,et al.  Stochastic discrimination , 1990, Annals of Mathematics and Artificial Intelligence.

[144]  Richard H. Crawford,et al.  Adapting Non-Uniform Rational B-Spline Fitting Approaches to Metamodeling , 2005 .

[145]  B. Bowerman,et al.  Forecasting, time series, and regression : an applied approach , 2005 .

[146]  Christine M. Anderson-Cook,et al.  Biostatistics: A Methodology for the Health Sciences (2nd ed.), Gerald van Belle, Lloyd D. Fisher, Patrick J. Heagerty, and Thomas Lumley , 2005 .

[147]  Cameron J. Turner,et al.  HyPerModels: Hyperdimensional Performance Models for engineering design , 2005 .

[148]  Richard H. Crawford,et al.  Selecting an Appropriate Metamodel: The Case for NURBs Metamodels , 2005, DAC 2005.

[149]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[150]  Dave Higdon,et al.  Combining Field Data and Computer Simulations for Calibration and Prediction , 2005, SIAM J. Sci. Comput..

[151]  Russell C. H. Cheng,et al.  Optimization by simulation metamodelling methods , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

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

[153]  Alyson G. Wilson,et al.  Integrated Analysis of Computer and Physical Experiments , 2004, Technometrics.

[154]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[155]  Carolyn Conner Seepersad,et al.  Building Surrogate Models Based on Detailed and Approximate , 2004, DAC 2004.

[156]  Richard H. Crawford,et al.  Metamodel defined multidimensional embedded sequential sampling criteria. , 2004 .

[157]  T. Simpson,et al.  Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

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

[159]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[160]  Richard H. Crawford,et al.  Generic Sequential Sampling for Metamodel Approximations , 2003 .

[161]  Russell R. Barton,et al.  Ch. 7. A review of design and modeling in computer experiments , 2003 .

[162]  J. Beck,et al.  Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation , 2002 .

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

[164]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[165]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

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

[167]  S L Zeger,et al.  The Evaluation of Multiple Surrogate Endpoints , 2001, Biometrics.

[168]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..

[169]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[170]  John W. Bandler,et al.  Editorial—Surrogate Modelling and Space Mapping for Engineering Optimization , 2001 .

[171]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[172]  R. Rebonato,et al.  The Most General Methodology to Create a Valid Correlation Matrix for Risk Management and Option Pricing Purposes , 2011 .

[173]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

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

[175]  Eugene M. Kleinberg,et al.  On the Algorithmic Implementation of Stochastic Discrimination , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[176]  Andreas Herrmann,et al.  Conjoint Measurement: Methods and Applications , 2000 .

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

[178]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[179]  Wei Chen,et al.  ROBUST CONCEPT EXPLORATION OF PROPULSION SYSTEMS WITH ENHANCED MODEL APPROXIMATION CAPABILITIES , 2000 .

[180]  Lih-Yuan Deng,et al.  Orthogonal Arrays: Theory and Applications , 1999, Technometrics.

[181]  Roger Woodard,et al.  Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.

[182]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[183]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[184]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[185]  Roger David Braddock,et al.  The use of graph theory in the sensitivity analysis of the model output: a second order screening method , 1999 .

[186]  J. C. Helton,et al.  Uncertainty and sensitivity analysis in performance assessment for the Waste Isolation Pilot Plant , 1999 .

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

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

[189]  K Wüthrich,et al.  Conformational analysis of protein and nucleic acid fragments with the new grid search algorithm FOUND , 1998, Journal of biomolecular NMR.

[190]  Susan T. Dumais,et al.  Inductive learning algorithms and representations for text categorization , 1998, CIKM '98.

[191]  Dennis K. J. Lin,et al.  On the identifiability of a supersaturated design , 1998 .

[192]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[193]  Kemper Lewis,et al.  The other side of multidisciplinary design optimization: Accomodating a multiobjective, uncertain and non-deterministic world , 1998 .

[194]  S. Gunn Support Vector Machines for Classification and Regression , 1998 .

[195]  Timothy M. Mauery,et al.  COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .

[196]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[197]  Raphael T. Haftka,et al.  Response Surface Approximations for Fatigue Life Prediction , 1997 .

[198]  Jack P. C. Kleijnen,et al.  Searching for important factors in simulation models with many factors: Sequential bifurcation , 1997 .

[199]  R. Haftka,et al.  Multidisciplinary aerospace design optimization: survey of recent developments , 1997 .

[200]  Timothy W. Simpson,et al.  On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments , 1997 .

[201]  E. Kleinberg An overtraining-resistant stochastic modeling method for pattern recognition , 1996 .

[202]  Richard J. Balling,et al.  Execution of multidisciplinary design optimization approaches on common test problems , 1996 .

[203]  Nam-Ky Nguyen An algorithmic approach to constructing supersaturated designs , 1996 .

[204]  B. Ripley Pattern Recognition and Neural Networks , 1996 .

[205]  Martin T. Hagan,et al.  Neural network design , 1995 .

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

[207]  Jack Dongarra,et al.  PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing , 1995 .

[208]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[209]  Russell R. Barton,et al.  Metamodeling: a state of the art review , 1994, Proceedings of Winter Simulation Conference.

[210]  James M. Lucas,et al.  How to Achieve a Robust Process Using Response Surface Methodology , 1994 .

[211]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

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

[213]  Vipin Kumar,et al.  Introduction to Parallel Computing , 1994 .

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

[215]  Dennis K. J. Lin A new class of supersaturated designs , 1993 .

[216]  H Rabitz,et al.  Systems Analysis at the Molecular Scale , 1989, Science.

[217]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[218]  A. Khuri A measure of rotatability for response-surface designs , 1988 .

[219]  Irwin Guttman,et al.  An index of rotatability , 1988 .

[220]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[221]  I. Jolliffe Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[222]  C. Nachtsheim Orthogonal Fractional Factorial Designs , 1985 .

[223]  Ronald L. Rardin,et al.  Technical Note - Searchability of the Composite and Multiple Surrogate Dual Functions , 1980, Oper. Res..

[224]  Sidney Addelman,et al.  trans-Dimethanolbis(1,1,1-trifluoro-5,5-dimethylhexane-2,4-dionato)zinc(II) , 2008, Acta crystallographica. Section E, Structure reports online.

[225]  David G. Stork,et al.  Pattern Classification , 1973 .

[226]  Jaime R. Carbonell,et al.  AI in CAI : An artificial intelligence approach to computer-assisted instruction , 1970 .

[227]  A. R. Manson,et al.  Minimum Bias Estimation and Experimental Design for Response Surfaces , 1969 .

[228]  K. H. Booth,et al.  Some Systematic Supersaturated Designs , 1962 .

[229]  J. S. Hunter,et al.  The 2 k — p Fractional Factorial Designs , 1961 .

[230]  G. Box,et al.  Some New Three Level Designs for the Study of Quantitative Variables , 1960 .

[231]  J. S. Hunter,et al.  Multi-Factor Experimental Designs for Exploring Response Surfaces , 1957 .