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[PDF] Top 20 Gaussian process regression of two nested computer codes

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Gaussian process regression of two nested computer codes

Gaussian process regression of two nested computer codes

... solutions of this ...formalism of GPR, and to minimize the sensitivity of the results to these ...spite of all these adaptations, when the input dimension be omes high ( d > 10 ), and when ... Voir le document complet

159

Multi-fidelity Gaussian process regression for computer experiments

Multi-fidelity Gaussian process regression for computer experiments

... problem of global sensitivity analysis for stochastic ...number of simulations. As the computer codes are time-consuming they are generally substituted by a surrogate ...are two sources ... Voir le document complet

307

Efficient sequential experimental design for surrogate modeling of nested codes

Efficient sequential experimental design for surrogate modeling of nested codes

... evaluations of each code are likely to be required to get a satisfying prediction ...case of two nested computer codes, which means that the output of the first code is an ... Voir le document complet

29

Calibration and prediction of two nested computer codes

Calibration and prediction of two nested computer codes

... number of observations increases. It can be seen that because of the stationarity of the Gaussian process modeling the nested phe- nomenon in the black-box method, which means ... Voir le document complet

36

GPSSI: Gaussian Process for Sampling Segmentations of Images

GPSSI: Gaussian Process for Sampling Segmentations of Images

... Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA ...tions of the region of interest which may have a significant impact on downstream ... Voir le document complet

9

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

... estimation of the smoothness ...log of the determinant of the Fisher information matrix, averaged over a Bayesian prior on the true covariance hyper-parameters, contain closely spaced ...crosses ... Voir le document complet

254

An automatic, multi-fidelity framework for optimizing the performance of super-cavitating hydrofoils using Gaussian process regression and Bayesian optimization

An automatic, multi-fidelity framework for optimizing the performance of super-cavitating hydrofoils using Gaussian process regression and Bayesian optimization

... This thesis accomplished its two main goals: (1) create a primer on machine learn- ing techniques applicable to engineering design problems including Gaussian proce[r] ... Voir le document complet

100

Computer modeling of a continuous manufacturing process

Computer modeling of a continuous manufacturing process

... Now that we have used the models to predict the effect of agglomerate size on drying rates, we can investigate the effects of the other process inputs on product quality a[r] ... Voir le document complet

65

Downscaling using Probabilistic Gaussian Copula Regression model.

Downscaling using Probabilistic Gaussian Copula Regression model.

... distribution of each predictand at the observed sites is represented by an appropriate probability density function (PDF), and then a regression model with outputs are parameters of the PDF is ... Voir le document complet

2

Canada and computer representations of design standards and building codes

Canada and computer representations of design standards and building codes

... one of several versions: author’s original, accepted manuscript or the publisher’s ...use of this website and the material on it are subject to the Terms and Conditions set forth ... Voir le document complet

15

Additive Kernels for Gaussian Process Modeling

Additive Kernels for Gaussian Process Modeling

... study of numerical simulators often deals with calculation intensive com- puter ...number of evaluations of the numerical simulator is limited and thus many methods such as uncertainty propagation, ... Voir le document complet

18

Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour

Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour

... modeling of functional-input regression problems A narrow class of R packages address regression with functional inputs ...majority of those packages rely on models limited by strong ... Voir le document complet

33

Multidimensional two-component Gaussian mixtures detection

Multidimensional two-component Gaussian mixtures detection

... In the previous section, we have applied the procedure proposed in Laurent et al. (2014) on the projection of the data on a given axis (generated by the empirical mean of the sample). Here, we alternatively ... Voir le document complet

30

On two variations of identifying codes

On two variations of identifying codes

... identifying codes, cycles, metric basis 1. Introduction Identifying codes are dominating sets having the property that any two vertices of the graph have distinct neighborhoods within the ... Voir le document complet

12

A condensed review of nuclear reactor thermal-hydraulic computer codes for two-phase flow analysis

A condensed review of nuclear reactor thermal-hydraulic computer codes for two-phase flow analysis

... A review is made of the computer codes developed in the U.S. for thermal-hydraulic analysis of nuclear reactors. The intention of this review is to compare these codes[r] ... Voir le document complet

138

Gaussian Process Modelling under Inequality Constraints

Gaussian Process Modelling under Inequality Constraints

... Structure of the manuscript 5 The framework proposed by Maatouk and Bay ( 2017 ) still presents some ...set of linear inequalities, the focus was on data interpolation with investigations for either ... Voir le document complet

219

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

... capacity of S m × Σ m to approximate the true value of (s, ...dimension of the model and corresponds to the amount of noise that we have to ...these two terms have to be small ...choice ... Voir le document complet

30

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

... capacity of S m × Σ m to approximate the true value of (s, ...dimension of the model and corresponds to the amount of noise that we have to ...these two terms have to be small ...choice ... Voir le document complet

27

Incremental construction of nested designs based on two-level fractional factorial designs

Incremental construction of nested designs based on two-level fractional factorial designs

... construction of suitable designs with high resolution has motivated intensive research since the pioneering papers [ 2 , 3 ...together two other ...columns of the design table. If this were not the ... Voir le document complet

36

Incremental Local Online Gaussian Mixture Regression for Imitation Learning of Multiple Tasks

Incremental Local Online Gaussian Mixture Regression for Imitation Learning of Multiple Tasks

... aspect of the demonstrations is if they contain enough information for the robot to determine what the relevant dimensions ...amount of demonstration needed is such that for each incorrect framing f there ... Voir le document complet

9

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