An efficient reliability method combining adaptive importance sampling and Kriging metamodel

Zhao, Hailong; Yue, Zhufeng; Liu, Yongshou; Gao, Zongzhan; Zhang, Yishang

doi:10.1016/j.apm.2014.10.015

Cited by 130 publications

(51 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second class of methods relies on the substitution of the original performance function by a surrogate model within a sampling-based scheme; a metamodel is in general orders of magnitude faster to be evaluated. In such metamodel-based approaches, Monte-Carlo sampling (AK-MCS [17]) or Importance Sampling (IS) techniques (AK-IS [13], MetaAK-IS 2 [14], KAIS [15], AK-SS [16]) are then used directly on the surrogate. Here, we propose a new method for the metamodel construction.…”

Section: 41mentioning

confidence: 99%

Computation of tail probabilities for non-classical gasdynamic phenomena

Razaaly

Congedo

2017

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. This paper presents a novel method for computing the tail probability of a given quantity of interest by using only a low number of evaluations of the computer model, representing the problem of interest under uncertainties. This method is then applied to the study of rarefaction shock waves (RSW) in a dense-gas shock tube. It is well-known in literature that the prediction of a RSW is highly sensitive to uncertainties on the initial flow conditions. The objective of this work is to compute a very accurate estimation of the RSW probability of occurrence in a shock tube configuration.

show abstract

Section: 41mentioning

confidence: 99%

Computation of tail probabilities for non-classical gasdynamic phenomena

Razaaly

Congedo

2017

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Some available works have investigated the Kriging‐based IS methods . Here, we will discuss the differences between the proposed method and the published studies.…”

Section: The Proposed Iterative Is Methodsmentioning

confidence: 99%

“…In works, the MCMC is used to sample a large population, and then, the K ‐means clustering algorithm is further employed to reduce this population to K clusters' center, and further, the experimental design is enriched and the accuracy of the estimated surrogate is updated until the target accuracy is achieved. It is evident that the accuracy of the MCMC is vital to these methods in other works . The MCMC has to meet with 2 issues .…”

Section: The Proposed Iterative Is Methodsmentioning

confidence: 99%

A surrogate‐based iterative importance sampling method for structural reliability analysis

Tang

Xia

2018

Quality & Reliability Eng

View full text Add to dashboard Cite

In this paper, a surrogate‐based iterative importance sampling (IS) method is proposed to efficiently evaluate failure probability (FP) of engineering structures. The surrogate is constructed by the training points centered at the most probable failure point (MPP), which is estimated by an approximate algorithm without any additional evaluations of the real limit state function (LSF), and the IS method using the estimated MPP is further obtained; and an iterative procedure combined with the surrogate and the IS method is finally proposed to improve the accuracy of the estimated result for the FP. Three numerical examples are employed to demonstrate the accuracy of the proposed method, and further, an inside flap of an aircraft is employed to demonstrate the practical application of the proposed method in engineering problems. The results show that the reasonable estimated result of the FP within the range of 10−3‐10−5 can be evaluated by the proposed method with only hundreds of the evaluations of the real LSF.

show abstract

“…Recent works in this area include (Kleijnen and Deflandre 2006), (Wu, Chen, Hu, Zhang, and Liang 2008), (Kleijnen 2009), (Ankenman, Nelson, and Staum 2010), (Khuri and Mukhopadhyay 2010), (Yin, Ng, and Ng 2011), (Razavi, Tolson, and Burn 2012), (Wei, Wu, and Chen 2012), and (Zhao, Yue, Liu, Gao, and Zhang 2014), to name just a few. Kleijnen (2009) presented a review of the Kriging metamodel.…”

Section: Metamodelingmentioning

confidence: 98%

Application of metamodeling to the valuation of large variable annuity portfolios

Gan¹

2015

2015 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

Variable annuities are long-term investment vehicles that have grown rapidly in popularity recently. One major feature of variable annuities is that they contain guarantees. The guarantees embedded in variable annuities are complex and the values of the guarantees cannot be obtained from closed-form formulas. Insurance companies rely heavily on Monte Carlo simulation to calculate the fair market values of the guarantees. Valuation and risk management of a large portfolio of variable annuities are a big challenge to insurance companies because the Monte Carlo simulation model is very time consuming. In this paper, we propose to use a metamodeling approach to speed up the valuation of large portfolios of variable annuities. Our numerical results show that the metamodeling approach can reduce the runtime significantly and produce accurate approximations.

show abstract

An efficient reliability method combining adaptive importance sampling and Kriging metamodel

Cited by 130 publications

References 26 publications

Computation of tail probabilities for non-classical gasdynamic phenomena

Computation of tail probabilities for non-classical gasdynamic phenomena

A surrogate‐based iterative importance sampling method for structural reliability analysis

Application of metamodeling to the valuation of large variable annuity portfolios

Contact Info

Product

Resources

About