Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction

From MaRDI portal
Publication:3142105

DOI10.2307/1269517zbMath0785.62025OpenAlexW4253834334MaRDI QIDQ3142105

Max D. Morris, Toby J. Mitchell, Donald Ylvisaker

Publication date: 26 April 1994

Full work available at URL: https://digital.library.unt.edu/ark:/67531/metadc1100641/




Related Items (71)

Estimation of high-order moment-independent importance measures for Shapley value analysisOptimal design for kernel interpolation: applications to uncertainty quantificationVariable-fidelity modeling of structural analysis of assembliesAugmented Gaussian random field: theory and computationAsymptotically optimum experimental designs for prediction of deterministic functions given derivative informationGeneralized polynomial chaos-informed efficient stochastic krigingAnalysis of finite-volume discrete adjoint fields for two-dimensional compressible Euler flowsOn the Instability Issue of Gradient-Enhanced Gaussian Process Emulators for Computer ExperimentsQuasi-regressionUnbiased quasi-regressionGaussian Process Prediction using Design-Based SubsamplingRegression Models Augmented with Direct Stochastic Gradient EstimatorsSurvey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functionsMonotone Emulation of Computer ExperimentsDesign of experiments for linear regression models when gradient information is availableSequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitationScaled Vecchia Approximation for Fast Computer-Model EmulationFinite-dimensional approximation of Gaussian processes with linear inequality constraints and noisy observationsGeneration of a cokriging metamodel using a multiparametric strategySliced Gradient-Enhanced Kriging for High-Dimensional Function ApproximationA Clustered Gaussian Process Model for Computer ExperimentsGroup symmetric Latin hypercube designs for symmetrical global sensitivity analysisProjection pursuit adaptation on polynomial chaos expansionsModified Penalized Blind Kriging for efficiently selecting a global trend modelA general construction for nested Latin hypercube designsCalibrating a large computer experiment simulating radiative shock hydrodynamicsA comparison of statistical emulation methodologies for multi‐wave calibration of environmental modelsInterpretable Architecture Neural Networks for Function VisualizationFully Bayesian Inference for Latent Variable Gaussian Process ModelsModified Active Subspaces Using the Average of GradientsOptimal experimental design and some related control problemsA Nonstationary Statistical Model for Computationally Intensive Numerical Ordinary Differential SystemsLocating Infinite Discontinuities in Computer ExperimentsFlexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer ModelDimension Reduction for Gaussian Process Emulation: An Application to the Influence of Bathymetry on Tsunami HeightsPhysics-Based Kriging Surrogates for a Class of Finite Element CodesComputer model validation with functional outputMachine learning aided static structural reliability analysis for functionally graded frame structuresRobust Gaussian stochastic process emulationLatent map Gaussian processes for mixed variable metamodelingMultifidelity Data Fusion via Gradient-Enhanced Gaussian Process RegressionUnbiased generalized quasi-regressionComparison of Gaussian process modeling softwareAn efficient method for constructing uniform designs with large sizeAdaptive sampling in hierarchical simulationComments on: process modeling for slope and aspect with application to elevation data mapsAnalysis methods for computer experiments: how to assess and what counts?A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetryEstimating Shape Constrained Functions Using Gaussian ProcessesKernel Approximation: From Regression to InterpolationHigh dimensional kriging metamodelling utilising gradient informationGaussian Process-Based Dimension Reduction for Goal-Oriented Sequential DesignExploratory designs for computational experimentsLong-Time Large Deviations for the Multiasset Wishart Stochastic Volatility Model and Option PricingIterative construction of Gaussian process surrogate models for Bayesian inferenceOn the choice of the low-dimensional domain for global optimization via random embeddingsGeneration of energy-minimizing point sets on spheres and their application in mesh-free interpolation and differentiationThe computational order of a DACE dynamical modelBayesian inverse regression for supervised dimension reduction with small datasetsOptimization of expensive black-box problems via gradient-enhanced KrigingA screening-based gradient-enhanced Kriging modeling method for high-dimensional problemsSensitivity-driven adaptive construction of reduced-space surrogatesThe BLUE in continuous-time regression models with correlated errorsEmulating Satellite Drag from Large Simulation ExperimentsObjective Bayesian Analysis of a Cokriging Model for Hierarchical Multifidelity CodesSpatial sampling design and covariance-robust minimax prediction based on convex design ideasOutput-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty QuantificationSome binary maximin distance designsHierarchical extended B-splines for approximations on sparse gridsComparison of designs for computer experimentsRepresentative points for distribution recovering




This page was built for publication: Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction