Metamodel-based importance sampling for structural reliability analysis
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In this paper, the authors propose to use a Kriging surrogate for the performance function as a means to build a quasi-optimal importance sampling density, which can be applied to analytical and finite element reliability problems and proves efficient up to 100 basic random variables.About:
This article is published in Probabilistic Engineering Mechanics.The article was published on 2013-07-01 and is currently open access. It has received 389 citations till now. The article focuses on the topics: Importance sampling & Random variable.read more
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LIF: A new Kriging based learning function and its application to structural reliability analysis
TL;DR: Results show that LIF and the new method proposed in this research are very efficient when dealing with nonlinear performance function, small probability, complicated limit state and engineering problems with high dimension.
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Polynomial-Chaos-based Kriging
TL;DR: PC-Kriging is derived as a new non-intrusive meta-modeling approach combining PCE and Kriging, which approximates the global behavior of the computational model whereas Kriged manages the local variability of the model output.
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An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability
TL;DR: The modification allows overcoming an important limitation of the original AK-IS in that it provides the algorithm with the flexibility to deal with multiple failure regions characterized by complex, non-linear limit states.
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Rare Event Estimation Using Polynomial-Chaos Kriging
TL;DR: A new structural reliability method based on the recently developed polynomial-chaos kriging (PC-kriging) approach coupled with an active learning algorithm known as adaptive kriged Monte Carlo simulation (AK-MCS) is developed.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
Cross-Validatory Choice and Assessment of Statistical Predictions
TL;DR: In this article, a generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription, and examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
Book
Monte Carlo Statistical Methods
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
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