[PDF] Top 20 Multi-fidelity Gaussian process regression for computer experiments
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Multi-fidelity Gaussian process regression for computer experiments
... variance for sequentially design the experiments [ Sacks et ...efficient for many cases, they can suffer from an important flaw when the accuracy of the kriging model is not homogeneous over the input ... Voir le document complet
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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
Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments
... term computer experiment means that the use of the computer model f mod , for obtaining the simulated value of a given phenomenon of interest at x, shares several characteristics with the classical ... Voir le document complet
254
Gaussian process regression with linear inequality constraints
... Keywords: Computer experiment, Gaussian process, Constrained regression, Sequential ...the computer experiments community, surrogate models are essential tools for the ... Voir le document complet
24
Bayesian analysis of hierarchical multi-fidelity codes.
... prediction for a code with s = 2 levels was suggested in ...a regression function whereas Qian and Wu [12] model it with a Gaussian ...distributions for the parameter ...distribution ... Voir le document complet
31
Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour
... the regression problem at ...distance for functions as structural parameters modifiable by the ...of computer experiments in [1], the ideal model configuration might likely depend on the ... Voir le document complet
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Approximate inference in related multi-output Gaussian Process Regression
... 3 Multi-output Gaussian Process Given a dataset for multiple outputs {(xi, yi)} for i ∈ [1; D] we define the joint output vector Y = [y1; y2; y3; ...xD]. For the sake of ... Voir le document complet
17
Sequential designs for sensitivity analysis of functional inputs in computer experiments
... linear regression (see Ramsay and Silverman [1]), a framework to ap- proximate, analyze and predict data with functional ...of experiments is not considered – data are assumed as already provided – and the ... Voir le document complet
19
Gaussian process regression of two nested computer codes
... whi h is hara terized by p 2 and u 2 , and the individual a tions of ea h input parameter, whi h are hara terized by the polynomial order p 1 ( onsidering dierent v alues of p 1 for ea h input ould eventually be ... Voir le document complet
159
Estimation and Test for Multi-Dimensional Regression Models
... 3.2. APPLICATION TO REAL TIME SERIES: POLLUTION OF OZONE Ozone is a reactive oxide, which is formed both in the stratosphere and troposphere. Near the surface of the ground, ozone is directly harmful to human health, ... Voir le document complet
22
Incremental Local Online Gaussian Mixture Regression for Imitation Learning of Multiple Tasks
... robotic experiments [1], [14], [2], [15], that the Gaussian Mixture Regression technique, introduced in [22], could be very successfully and easily used for encoding demonstrations through a ... Voir le document complet
9
Multi-Scale Network Regression for Brain-Phenotype Associations
... Network Regression MSNR, a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by interpretable modeling.. Cap[r] ... Voir le document complet
15
Multi-fidelity Robust Design Optimization Methods for Organic Rankine Cycles
... the multi- objective genetic algorithm (MOGA), in order to generate the BK surrogates, which can be efficiently parallelized on a high-performance computer, while additional O(10 × n) evaluations are ... Voir le document complet
285
An efficient methodology for modeling complex computer codes with Gaussian processes
... Complex computer codes are often too time expensive to be directly used to perform uncertainty propagation studies, global sensitivity analysis or to solve op- timization ...complex computer code by a ... Voir le document complet
30
Downscaling using Probabilistic Gaussian Copula Regression model.
... The 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 employed. ... Voir le document complet
2
Wasserstein regularization for sparse multi-task regression
... However, experiments show a degraded performance as the overlap between the supports of relevant regressors ...sparse multi-task regression with outlier tasks and outlier features (non-overlapping ... Voir le document complet
16
GPSSI: Gaussian Process for Sampling Segmentations of Images
... prerequisite for clin- ical ...method for producing such image segmentation samples from a single expert segmentation is ...a Gaussian process, which leads to segmentations that are spatially ... Voir le document complet
9
Group kernels for Gaussian process metamodels with categorical inputs
... T prox can be chosen as in Section 2.2.1 by a warping. Indeed, the split between groups may cor- respond to jumps in the warping curve F . Intuitively, the estimated distance |F (u + 1) − F (u)| between levels u and u + ... Voir le document complet
35
Gaussian Process Modelling under Inequality Constraints
... approach for truncated multinormals introduced in ( Pakman and Paninski , 2014 ), which follows the same dynamics as a classical HMC sampler, but the particle “bounces” on the boundaries if its trajectory reaches ... Voir le document complet
219
Unfolding in the Empirical Sciences: Experiments, Thought-experiments and Computer Simulations
... 4. Experiments as unfolders E, when successfully carried out, provide experimental knowledge about physical systems, that is, true statements describing how some unambiguously designed systems behave and obtained ... Voir le document complet
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