Surrogate-assisted global sensitivity analysis: an overview

Cheng, Kai; Lü, Zhenzhou; Ling, Chunyan; Zhou, Suting

doi:10.1007/s00158-019-02413-5

Cited by 82 publications

(19 citation statements)

References 157 publications

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“…It is done by solving a likelihood optimisation problem. For more details about the theoretical background, the readers are invited to refer to Sacks et al (1989), Paulson and Ragkousis (2015), Jones et al (1998), Kleijnen (2017) and Cheng et al (2020). In the present study, the Kriging meta-models are build based on the ordinary Kriging using pyKriging which is an open source Python Kriging toolkit (Paulson and Ragkousis 2015).…”

Section: Kriging: Some Fundamentalsmentioning

confidence: 99%

“…To cope with this issue, a strategy based on surrogate modelling is adopted. A surrogate model, also called meta-model, has the following basic idea: from a small number of evaluations of an expensive model, a mathematical function which gives the same output for the same inputs is constructed (Cheng et al 2020). Different methods allow to do so as Kriging (Sacks et al 1989;Paulson and Ragkousis 2015), polynomial chaos (Wiener 1938;Xiu and Karniadakis 2002;Totis and Sortino 2020), support vector regression (Smola and Schölkopf 2004), etc.…”

Section: Introductionmentioning

confidence: 99%

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Geometric design of friction ring dampers in blisks using nonlinear modal analysis and Kriging surrogate model

Sun

Denimal

Yuan

et al. 2022

Struct Multidisc Optim

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Integrally bladed disks (blisk) have been widely used in the turbo-machinery industry due to its high aerodynamic performance and structural efficiency. A friction ring damper (FRD) is usually integrated in the system to improve its low damping. However, the design of the geometry of this FRD become complex and computationally expensive due to the strong nonlinearities from friction interfaces. In this work, we propose an efficient modelling strategy based on advanced nonlinear modal analysis and Kriging surrogate models to design and optimize the geometry of a 3D FRD attached to a high fidelity full-scale blisk. The 3D ring damper is parametrised with a few key geometrical parameters. The impact of each geometric parameter and their sensitivities to nonlinear dynamic response can be efficiently assessed using Kriging meta-modelling based on a few damped nonlinear normal modes. Results demonstrate that the damping performances of ring dampers can be substantially optimized through the proposed modelling strategy whilst key insights for the design of the rings are given. It is also demonstrated that the distribution of the contact normal load on the contact interfaces has a strong influence on the damping performances and can be effectively tuned via the upper surface geometry of the ring dampers.

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Section: Kriging: Some Fundamentalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric design of friction ring dampers in blisks using nonlinear modal analysis and Kriging surrogate model

Sun

Denimal

Yuan

et al. 2022

Struct Multidisc Optim

View full text Add to dashboard Cite

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“…Subsequent work by Jin et al [2004] considered a similar approach that exploited a tensor-product kernel to simplify the integration problems required, though this work did not consider the posterior covariance in their estimator. See Cheng et al [2020] for a more extensive review of emulation-based global sensitivity analysis techniques, and Girard et al [2016], Beddows et al [2017], Renardy et al [2018] for a survey of applications of such approaches. One could envisage an analogous emulation strategy for local sensitivity analysis of computationally expensive models that involves first constructing an emulator ĝ of the objective function g and then evaluating the derivative dĝ dp (p * ) which, assuming a conducive emulator, can be computed at a lower cost than the derivative of g itself.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Gradients for Fast Calibration of Differential Equation Models

Cockayne¹,

Duncan²

2021

SIAM/ASA J. Uncertainty Quantification

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Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottleneck in state-of-the art calibration methods is the calculation of local sensitivities, i.e. derivatives of the loss function with respect to the estimated parameters, which often necessitates several numerical solves of the underlying system of partial or ordinary differential equations. In this paper we present a new probabilistic approach to computing local sensitivities. The proposed method has several advantages over classical methods. Firstly, it operates within a constrained computational budget and provides a probabilistic quantification of uncertainty incurred in the sensitivities from this constraint. Secondly, information from previous sensitivity estimates can be recycled in subsequent computations, reducing the overall computational effort for iterative gradient-based calibration methods. The methodology presented is applied to two challenging test problems and compared against classical methods.

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“…In a multidisciplinary context, the mainstream global sensitivity analysis (SA) focuses on model outputs [6]. In civil engineering, the global SA is used to study model outputs of surrogate models without a direct relationship to Pf, with the results reflecting model simplifications of approximation methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

Kala

2021

Mathematics

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This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1-Pf) and discrete entropy—Pfln(Pf)-(1-Pf)ln(1-Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

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Surrogate-assisted global sensitivity analysis: an overview

Cited by 82 publications

References 157 publications

Geometric design of friction ring dampers in blisks using nonlinear modal analysis and Kriging surrogate model

Geometric design of friction ring dampers in blisks using nonlinear modal analysis and Kriging surrogate model

Probabilistic Gradients for Fast Calibration of Differential Equation Models

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

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