Clustered active-subspace based local Gaussian process emulator for high-dimensional and complex computer models (Q2134712)

scientific article; zbMATH DE number 7517113

Language	Label	Description	Also known as
English	Clustered active-subspace based local Gaussian process emulator for high-dimensional and complex computer models	scientific article; zbMATH DE number 7517113

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instance of

scholarly article

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title

Clustered active-subspace based local Gaussian process emulator for high-dimensional and complex computer models (English)

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Journal of Computational Physics

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publication date

3 May 2022

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full work available at URL

https://arxiv.org/abs/2101.00057

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zbMATH Keywords

dimension reduction

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Gaussian process emulator

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clustering

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uncertainty quantification

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describes a project that uses

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A survey of cross-validation procedures for model selection

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Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations

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Sequential design of computer experiments for the estimation of a probability of failure

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Large-Scale PDE-Constrained Optimization: An Introduction

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Multi-output local Gaussian process regression: applications to uncertainty quantification

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Q4642510

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Active Subspaces

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Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces

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Comment

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Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation

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A radial basis function method for global optimization

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Sliced Inverse Regression for Dimension Reduction

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Dimension Reduction for Gaussian Process Emulation: An Application to the Influence of Bathymetry on Tsunami Heights

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An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations

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Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach

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Data driven governing equations approximation using deep neural networks

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Constrained global optimization of expensive black box functions using radial basis functions

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Design and analysis of computer experiments. With comments and a rejoinder by the authors

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Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation

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Q4856771

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Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

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Gaussian process surrogates for failure detection: a Bayesian experimental design approach

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Q3394879

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A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

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Q3538690

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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When Bifidelity Meets CoKriging: An Efficient Physics-Informed MultiFidelity Method

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2021.110840

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Sitelinks

Mathematics(1 entry)

mardi Publication:2134712