A novel Nested Stochastic Kriging model for response noise quantification and reliability analysis

Hao, Peng; Feng, Shaojun; Líu, Hao; Wang, Yutian; Wang, Bo; Wang, Bin

doi:10.1016/j.cma.2021.113941

Cited by 34 publications

(5 citation statements)

References 54 publications

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“…2 that, in terms of variance estimation, both approaches similarly fail to capture the true variability. This issue has been recognized and documented in the SK literature [19,36].…”

Section: Known Intrinsic Noisementioning

confidence: 92%

“…With these weights, the optimal linear estimator and MSE are directly given by Eqs. (19) and (20), respectively. Furthermore, following that Z is a zero-mean stochastic process, i.e.…”

Section: Lar-pce Stochastic Krigingmentioning

confidence: 99%

“…Defined as an extension of Kriging, SK accounts for heteroscedastic noise in the response by estimating the sample variance at each point of the ED using multiple runs of the simulator for each specific input. The effectiveness of SK has been demonstrated on a broad range of applications, including risk and reliability analysis [18,19], industrial welding processes [20], and game theoretic simulations [21], to mention a few.…”

Section: Introductionmentioning

confidence: 99%

“…In a similar vein, Hao et al [19] introduced a Nested SK model, which decouples in an iterative way the response noise parameters from the deterministic Kriging parameters used for calibrating the meta-model. In a similar context, Cheng et al [25] proposed a generalized polynomial chaos-stochastic Kriging (gPCE SK) approach, which combines the benefits of PCE and SK.…”

Section: Introductionmentioning

confidence: 99%

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Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems

García-Merino

Calvo‐Jurado

Martínez‐Pañeda

et al. 2023

Applied Mathematical Modelling

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“…2 that, in terms of variance estimation, both approaches similarly fail to capture the true variability. This issue has been recognized and documented in the SK literature [19,36].…”

Section: Known Intrinsic Noisementioning

confidence: 92%

“…With these weights, the optimal linear estimator and MSE are directly given by Eqs. (19) and (20), respectively. Furthermore, following that Z is a zero-mean stochastic process, i.e.…”

Section: Lar-pce Stochastic Krigingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems

García-Merino

Calvo‐Jurado

Martínez‐Pañeda

et al. 2023

Applied Mathematical Modelling

View full text Add to dashboard Cite

“…where R x (i) , x (j) is a spatial correlation function, indicating the spatial correlation between sample points x (i) and x (j) , which plays a decisive role in the accuracy of the simulation [30].…”

Section: Krigingmentioning

confidence: 99%

Optimization Method Based on Hybrid Surrogate Model for Pulse-Jet Cleaning Performance of Bag Filter

et al. 2023

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The pulse-jet cleaning process is a critical part of the bag filter workflow. The dust-cleaning effect has a significant impact on the operating stability of bag filters. Aiming at the multi-parameter optimization problem involved in the pulse-jet cleaning process of bag filters, the construction method of hybrid surrogate models based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging sub-surrogate models is investigated. With four sub-surrogate model hybrid modes, the corresponding hybrid surrogate models, namely PR-HSM, PK-HSM, RK-HSM, and PRK-HSM, are constructed for the multi-parameter optimization involved in the pulse-jet cleaning process of bag filters, and their objective function is the average pressure on the inner side wall of the filter bag at 1 m from the bag bottom. The genetic algorithm is applied to search for the optimal parameter combination of the pulse-jet cleaning process. The results of simulation experiments and optimization calculations show that compared with the sub-surrogate model PRSM, the evaluation indices RMSE, R2, and RAAE of the hybrid surrogate model RK-HSM are 9.91%, 4.41%, and 15.60% better, respectively, which greatly enhances the reliability and practicability of the hybrid surrogate model. After using the RK-HSM, the optimized average pressure F on the inner side wall of the filter bag at 1 m from the bag bottom is −1205.1605 Pa, which is 1321.4543 Pa higher than the average pressure value under the initial parameter condition set by experience, and 58.4012 Pa to 515.2836 Pa higher than using the three sub-surrogate models, verifying its usefulness.

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Simulation Optimization of Medical Decision Making

Yang,

Yao

2023

SpringerBriefs in Service Science

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A novel Nested Stochastic Kriging model for response noise quantification and reliability analysis

Cited by 34 publications

References 54 publications

Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems

Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems

Optimization Method Based on Hybrid Surrogate Model for Pulse-Jet Cleaning Performance of Bag Filter

Simulation Optimization of Medical Decision Making

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