Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions

By B. Bichon, Michael S. Eldred, Laura Painton Swiler, Sankaran Mahadevan, John M. McFarland. 2008

1,133 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f36249ffec9a5f01557d630d7937ece801d74a27

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

LIF: A new Kriging based learning function and its application to structural reliability analysis (2017, methodology): “Various kinds of sequential DoE based on the Kriging model have been proposed to improve the accuracy of structural reliability analysis and reduce the number of calls to the real performance function [8,29,32,38,41,42].”
A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis (2016, background): “2, the accuracy of the one-dimensional classification problem is mainly affected by the accuracy near the crossing points [31,32].”
Active learning for structural reliability: Survey, general framework and benchmark (2021, methodology): “One of the first implementations of this flowchart was proposed by Bichon et al. (2008) in their efficient global reliability analysis method (EGRA).”
Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities (2017, methodology): “To reduce the epistemic uncertainty (bias and variance) introduced by the surrogate model, we need to adaptively determine the optimal input settings for computer simulations and thus maximize the information we obtained from the simulation model for training the surrogate model [16, 109].”
A survey on approaches for reliability-based optimization (2010, methodology): “For example, in Bichon et al (2008), meta-models for reliability analysis are trained using the so-called Efficient Global Optimization procedure, which allows selecting the samples to train the meta-model adaptively in order to ensure accuracy in the vicinity of the limit state function.”
Polynomial-Chaos-based Kriging (2015, positive): “This characteristics is exploited when devising adaptive experimental designs in structural reliability analysis, see Echard et al. (2013); Bichon et al. (2008, 2011); Dubourg et al. (2011).”
Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output (2008, methodology): “Gaussian process models are increasingly being used as surrogates to expensive computer simulations for the purposes of optimization and uncertainty propagation [10,14–18].”
A new adaptive response surface method for reliability analysis (2013, background): “Recent contributions have been made on kriging [37,14,4] and support vector machine”
Sequential design of computer experiments for the estimation of a probability of failure (2010, methodology): “The same leading principle motivates the criteria proposed by Ranjan et al. (2008) and Bichon et al. (2008), which can be seen as special cases of the following sampling criterion: JRBn (x) := En ( max ( 0, ǫ(x)δ − |u− ξ(x)|δ )) , (31) where ǫ(x) = κσn(x), κ, δ > 0.”