Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version | Paper Citation Record

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This paper cites Anisotropy models for spatial data.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Anisotropy models for spatial data

Reference 1

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This paper cites Performance evaluation of an advanced local search evo- lutionary algorithm.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Performance evaluation of an advanced local search evo- lutionary algorithm

Reference 2

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This paper cites Adaptive control processes: a guided tour.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Adaptive control processes: a guided tour

Reference 3

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This paper cites Sequential dimension reduction for learning features of expensive black-box functions.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Sequential dimension reduction for learning features of expensive black-box functions

Reference 4

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This paper cites The proper orthogonal decomposition in the analysis of turbulent ﬂows.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version The proper orthogonal decomposition in the analysis of turbulent ﬂows

Reference 5

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This paper cites A warped kernel improving ro- bustness in Bayesian optimization via random embeddings.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A warped kernel improving ro- bustness in Bayesian optimization via random embeddings

Reference 6

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This paper cites On the choice of the low-dimensional domain for global optimization via random embeddings.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version On the choice of the low-dimensional domain for global optimization via random embeddings

Reference 7

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This paper cites Im- proving kriging surrogates of high-dimensional design models by partial least squares dimension reduction.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Im- proving kriging surrogates of high-dimensional design models by partial least squares dimension reduction

Reference 8

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This paper cites Investigation of adaptive design variables bounds in dimensionality reduction for aerodynamic shape optimization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Investigation of adaptive design variables bounds in dimensionality reduction for aerodynamic shape optimization

Reference 9

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This paper cites Shapes of embedded minimal surfaces.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Shapes of embedded minimal surfaces

Reference 10

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This paper cites Active subspace methods in theory and practice: applications to kriging surfaces.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Active subspace methods in theory and practice: applications to kriging surfaces

Reference 11

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This paper cites Active shape models-their training and application.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Active shape models-their training and application

Reference 12

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This paper cites Statistics for spatial data.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Statistics for spatial data

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This paper cites Package ‘kergp’.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Package ‘kergp’

Reference 14

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This paper cites ´Etude de classes de noyaux adapt´ ees ` a la simpliﬁcation et ` a l’interpr´ etation des mod` eles d’approximation.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version ´Etude de classes de noyaux adapt´ ees ` a la simpliﬁcation et ` a l’interpr´ etation des mod` eles d’approximation

Reference 15

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This paper cites Additive covariance kernels for high-dimensional Gaussian process modeling.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Additive covariance kernels for high-dimensional Gaussian process modeling

Reference 16

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This paper cites Additive Gaussian processes.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Additive Gaussian processes

Reference 17

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This paper cites Recent advances in surrogate-based optimization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Recent advances in surrogate-based optimization

Reference 18

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This paper cites A statistical view of some chemometrics regression tools.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A statistical view of some chemometrics regression tools

Reference 19

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This paper cites An algorithm for ﬁnding intrinsic dimensionality of data.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version An algorithm for ﬁnding intrinsic dimensionality of data

Reference 20

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This paper cites Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front -- Extended Version.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front -- Extended Version

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This paper cites Principal component analysis.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Principal component analysis

Reference 22

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This paper cites A taxonomy of global optimization methods based on response surfaces.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A taxonomy of global optimization methods based on response surfaces

Reference 23

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This paper cites Eﬃcient Global Optimization of expensive black-box functions.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Eﬃcient Global Optimization of expensive black-box functions

Reference 24

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This paper cites A data-based approach for fast airfoil analysis and optimization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A data-based approach for fast airfoil analysis and optimization

Reference 25

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This paper cites Surrogate-based aerodynamic shape optimization with the active subspace method.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Surrogate-based aerodynamic shape optimization with the active subspace method

Reference 26

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This paper cites On the limited memory BFGS method for large scale optimiza- tion.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version On the limited memory BFGS method for large scale optimiza- tion

Reference 27

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This paper cites Choosing the sample size of a computer experiment: A practical guide.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Choosing the sample size of a computer experiment: A practical guide

Reference 28

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This paper cites Genetic optimization using derivatives: the rgenoud package for R.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Genetic optimization using derivatives: the rgenoud package for R

Reference 29

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This paper cites Kernel PCA and de-noising in feature spaces.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Kernel PCA and de-noising in feature spaces

Reference 30

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This paper cites On Bayesian methods for seeking the extremum.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version On Bayesian methods for seeking the extremum

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This paper cites Test functions for optimization needs.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Test functions for optimization needs

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This paper cites Kriging surrogate model with co- ordinate transformation based on likelihood and gradient.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Kriging surrogate model with co- ordinate transformation based on likelihood and gradient

Reference 33

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This paper cites On the accuracy of kriging model in active subspaces.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version On the accuracy of kriging model in active subspaces

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This paper cites Towards a space re- duction approach for eﬃcient structural shape optimization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Towards a space re- duction approach for eﬃcient structural shape optimization

Reference 35

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This paper cites Numerical assessment of springback for the deep drawing process by level set interpolation using shape manifolds.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Numerical assessment of springback for the deep drawing process by level set interpolation using shape manifolds

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This paper cites Gaussian Processes for Machine Learn- ing.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Gaussian Processes for Machine Learn- ing

Reference 37

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This paper cites DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and opti- mization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and opti- mization

Reference 38

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This paper cites Design and analysis of computer experiments.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Design and analysis of computer experiments

Reference 39

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This paper cites Sensitivity analysis in practice: a guide to assessing scientiﬁc models.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Sensitivity analysis in practice: a guide to assessing scientiﬁc models

Reference 40

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This paper cites Kernel principal component analysis.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Kernel principal component analysis

Reference 41

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This paper cites Unbounded Bayesian opti- mization via regularization.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Unbounded Bayesian opti- mization via regularization

Reference 42

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This paper cites Space exploration and global optimization for computation- ally intensive design problems: a rough set based approach.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Space exploration and global optimization for computation- ally intensive design problems: a rough set based approach

Reference 43

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This paper cites Survey of modeling and optimization strategies to solve high- dimensional design problems with computationally-expensive black-box functions.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Survey of modeling and optimization strategies to solve high- dimensional design problems with computationally-expensive black-box functions

Reference 44

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This paper cites A brief introduction to statistical shape analysis.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A brief introduction to statistical shape analysis

Reference 45

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This paper cites Interpolation of spatial data: some theory for kriging.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Interpolation of spatial data: some theory for kriging

Reference 46

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This paper cites Gaussian processes with built-in di- mensionality reduction: Applications to high-dimensional uncertainty propagation.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Gaussian processes with built-in di- mensionality reduction: Applications to high-dimensional uncertainty propagation

Reference 47

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This paper cites The nature of statistical learning theory.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version The nature of statistical learning theory

Reference 48

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This paper cites Singular value decomposition and principal component analysis.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Singular value decomposition and principal component analysis

Reference 49

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This paper cites Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

Reference 50

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This paper cites Bayesian optimization in high dimensions via random embeddings.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Bayesian optimization in high dimensions via random embeddings

Reference 51

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Observation e841ac0d-681b-4dc4-a9e3-16083d8e57ba · outbound

This paper cites A developed surrogate-based optimization framework combining HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version A developed surrogate-based optimization framework combining HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:24:05.351151Z

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Observation 8f24854d-406c-4b1e-832d-c3a9f5c8ea1f · outbound

This paper cites Penalized Gaussian process regression and classiﬁcation for high- dimensional nonlinear data.

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version Penalized Gaussian process regression and classiﬁcation for high- dimensional nonlinear data

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:24:05.334654Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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