On the ensemble of metamodels with multiple regional optimized weight factors

Yin, Hanfeng; Fang, Hsu Wei; Wen, Guilin; Gutowski, Matthew; Xiao, Youye

doi:10.1007/s00158-017-1891-1

Cited by 30 publications

(16 citation statements)

References 29 publications

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“…(2018), and Yin et al . (2018) are more time consuming than each individual surrogate they used to create the ensemble. Whereas, we create ensembles which are less time consuming than individual surrogates and have less inconsistency.…”

Section: Resultsmentioning

confidence: 99%

“…Also, Habib et al (2017) use an EoS-assisted optimization method and evaluate it at various levels of fidelity. Yin et al (2018) propose assembling an EoS by dividing the design space into several subspaces such that each is allocated a collection of optimized weights. Acar (2015) argues for giving greater importance to maximum error than RMSE by assigning weights of the individual surrogates in the EoS.…”

Section: Frame Of Referencementioning

confidence: 99%

See 1 more Smart Citation

Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

Alizadeh

Jia

Nellippallil

et al. 2019

AIEDAM

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In engineering design, surrogate models are often used instead of costly computer simulations. Typically, a single surrogate model is selected based on the previous experience. We observe, based on an analysis of the published literature, that fitting an ensemble of surrogates (EoS) based on cross-validation errors is more accurate but requires more computational time. In this paper, we propose a method to build an EoS that is both accurate and less computationally expensive. In the proposed method, the EoS is a weighted average surrogate of response surface models, kriging, and radial basis functions based on overall cross-validation error. We demonstrate that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions. We demonstrate the use of an EoS using hot rod rolling as an example. Finally, we include a rule-based template which can be used for other problems with similar requirements, for example, the computational time, required accuracy, and the size of the data.

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Section: Resultsmentioning

confidence: 99%

Section: Frame Of Referencementioning

confidence: 99%

Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

Alizadeh

Jia

Nellippallil

et al. 2019

AIEDAM

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show abstract

“…In addition, a random load model based on the Gaussian distribution function was constructed to simulate the randomness of the working load fluctuation. These methods were combined to construct an optimization algorithm based on the Kriging surrogate model [21] and NSGA-II. The concrete flowchart of the algorithm is illustrated in Figure 3.…”

Section: Optimization Based On the Kriging Surrogate Model-nsga-ii Almentioning

confidence: 99%

Design Optimization of a VX Gasket Structure for a Subsea Connector Based on the Kriging Surrogate Model-NSGA-II Algorithm Considering the Load Randomness

Zeng

Ren

Yu³

et al. 2019

Algorithms

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The VX gasket is an important part of the wellhead connector for a subsea Christmas tree. Optimization of the gasket’s structure can improve the connector’s sealing performance. In this paper, we develop an optimization approach for the VX gasket structure, taking into consideration working load randomness, based on the Kriging surrogate model-NSGA-II algorithm. To guarantee the simulation accuracy, a random finite element (R-FE) model of the connector’s sealing structure was constructed to calculate the gasket’s sealing performance under random working load conditions. The working load’s randomness was simulated using the Gaussian distribution function. To improve the calculation efficiency of the sealing performance for individuals within the initial populations, Kriging surrogate models were constructed. These models accelerated the optimization speed, where the training sample was obtained using an experimental method design and the constructed R-FE model. The effectiveness of the presented approach was verified in the context of a subsea Christmas tree wellhead connector, which matched the 20'' casing head. The results indicated that the proposed method is effective for VX gasket structure optimization in subsea connectors, and that efficiency was significantly enhanced compared to the traditional FE method.

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“…Local RSM methods such as the moving least square technique were developed to handle highly nonlinear limit state functions [31]. Other commonly used surrogate modeling methods have also been developed over the years, such as artificial neural networks (ANN) [32][33][34][35][36][37], Kriging [38][39][40][41][42][43][44][45][46], high-dimensional or factorized high-dimensional model representation [47][48][49][50][51], support vector machine [52][53][54][55][56][57], radial basis functions (RBFs) [58], and even ensemble of surrogates [59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis Based on Surrogate Modeling Methods

Wang¹

2020

Reliability and Maintenance - An Overview of Cases

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Various surrogate modeling methods have been developed to generate approximate functions of expensive numerical simulations. They can be used in reliability analysis when integrated with a numerical reliability analysis method such as a first-order or second-order reliability analysis method (FORM/SORM), or Monte Carlo simulations (MCS). In this chapter, a few surrogate modeling methods are briefly reviewed. A reliability analysis approach using surrogate models based on radial basis functions (RBFs) and successive RBFs is presented. The RBF surrogate modeling method is a special type of interpolation method, as the model passes through all available sample points. Augmented RBFs are adopted to create approximate models of a limit state/performance function, before the failure probability can be computed using MCS. To improve model efficiency, a successive RBF (SRBF) surrogate modeling method is investigated. Several mathematical and practical engineering examples are solved. The failure probabilities computed using the SRBF surrogate modeling method are fairly accurate, when a reasonable sample size is used to create the surrogate models. The method based on augmented RBF surrogate models is useful for probabilistic analysis of practical problems, such as civil and mechanical engineering applications.

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On the ensemble of metamodels with multiple regional optimized weight factors

Cited by 30 publications

References 29 publications

Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

Design Optimization of a VX Gasket Structure for a Subsea Connector Based on the Kriging Surrogate Model-NSGA-II Algorithm Considering the Load Randomness

Reliability Analysis Based on Surrogate Modeling Methods

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