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Adaptive experiment design for probabilistic integration · Study

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ArticleAdaptive experiment design for probabilistic integration

Pengfei Wei2020

Article2020

Adaptive experiment design for probabilistic integration

Pengfei Wei · Xing Zhang · Michael Beer

49 citations·0/0 files stored·Computer Methods in Applied Mechanics and Engineering

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Probabilistic and Robust Engineering Design Mathematical Approximation and Integration Gaussian Processes and Bayesian Inference

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References

2007Simulation and the Monte Carlo Method 2001Estimation of small failure probabilities in high dimensions by subset simulation 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 1994Optimal Latin-hypercube designs for computer experiments 2013Metamodel-based importance sampling for structural reliability analysis 2004A critical appraisal of reliability estimation procedures for high dimensions 2009Do Rosenblatt and Nataf isoprobabilistic transformations really differ?2017Sensitivity estimation of failure probability applying line sampling

Cited by

2024A sequential sampling-based Bayesian numerical method for reliability-based design optimization 2021Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration 2024On active learning for Gaussian process-based global sensitivity analysis 2023Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis 2020Bayesian probabilistic propagation of imprecise probabilities with large epistemic uncertainty

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Details

Year2020

Typearticle

Published2020-04-11

VenueComputer Methods in Applied Mechanics and Engineering

ISSN-L0045-7825

Languageen

Citations49

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.cma.2020.113035

openalex_workW3016239741

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2021The soft Monte Carlo method

2024Hybrid uncertainty propagation based on multi-fidelity surrogate model

2022Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion