Global sensitivity analysis using support vector regression
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TLDR
A new kernel function derived from orthogonal polynomials is proposed for support vector regression (SVR), based on which the Sobol’ global sensitivity indices can be computed analytically by the coefficients of the surrogate model built by SVR.About:
This article is published in Applied Mathematical Modelling.The article was published on 2017-09-01 and is currently open access. It has received 81 citations till now. The article focuses on the topics: High-dimensional model representation & Polynomial kernel.read more
Citations
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Microstructural evolution and support vector regression model for an aged Ni-based superalloy during two-stage hot forming with stepped strain rates
TL;DR: In this paper, the microstructure changes and high-temperature flow characteristics of a Ni-based superalloy containing δ phases were researched by isothermal two-stage hightemperature compression experiments with stepped strain rates.
Journal ArticleDOI
Reliability analysis with stratified importance sampling based on adaptive Kriging
TL;DR: A stratified importance sampling method with an adaptive Kriging strategy to estimate failure probabilities and the efficiency of the proposed method is demonstrated using several analytic examples and then transferred to a carbon dioxide storage benchmark problem.
Journal ArticleDOI
Surrogate-assisted global sensitivity analysis: an overview
TL;DR: An overview of surrogate model approaches with an emphasis of their application for variance-based global sensitivity analysis, including polynomial regression model, high-dimensional model representation, state-dependent parameter, Polynomial chaos expansion, Kriging/Gaussian Process, support vector regression, radial basis function, and low rank tensor approximation are presented.
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Sparse polynomial chaos expansion based on D-MORPH regression
Kai Cheng,Zhenzhou Lu +1 more
TL;DR: Results show that the developed method is superior to the LAR-based sparse PCE in terms of efficiency and accuracy.
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A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices
TL;DR: Bayesian Monte Carlo method is employed for developing a new technique to estimate the Sobol' indices with low computational cost, and the results show that the newly developing technique is comparable to the sparse polynomial chaos expansion and Quasi-Monte Carlo method.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
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Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
TL;DR: In this article, global sensitivity indices for rather complex mathematical models can be efficiently computed by Monte Carlo (or quasi-Monte Carlo) methods, which are used for estimating the influence of individual variables or groups of variables on the model output.