9 results on '"active learning function"'
Search Results
2. A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model.
- Author
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Zhi, Pengpeng, Yun, Guoli, Wang, Zhonglai, Shi, Peijing, Guo, Xinkai, Wu, Jiang, and Ma, Zhao
- Subjects
FAILURE mode & effects analysis ,GAUSSIAN processes ,ACTIVE learning ,SURFACE states ,QUADRILATERALS - Abstract
In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. AK-Gibbs: An active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities.
- Author
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Zhang, Wei, Zhao, Ziyi, Xu, Huanwei, Li, Xiaoyu, and Wang, Zhonglai
- Subjects
- *
GIBBS sampling , *ACTIVE learning , *FAILURE (Psychology) , *PROBABILITY density function , *PROBABILITY theory , *KRIGING , *HIGH-dimensional model representation - Abstract
• This study proposes an AK-Gibbs method for small failure probabilities with nonlinear and time-consuming performance function. • EALF function directly linked to global error is constructed. • IEALF function based on proposed EALF and joint probability density function is proposed. • Gibbs importance sampling algorithm is derived based on Gibbs algorithm aims to effectively establish the candidate importance sample pool. • The AK-Gibbs is more efficient and accuracy than other prevailing methods for small failure probabilities. In engineering practices, it is a time-consuming procedure to estimate the small failure probability of highly nonlinear and dimensional limit state functions and Kriging-based methods are more effective representatives. However, it is an important challenge to construct the candidate importance sample pool for Kriging-based small failure probability analysis methods with multiple input random variables when the Metropolis-Hastings (M-H) algorithm with acceptance-rejection sampling principle is employed. To address the challenge and estimate the reliability of structures in a more efficient and accurate way, an active learning Kriging model based on the Gibbs importance sampling algorithm (AK-Gibbs) is proposed, especially for the small failure probabilities with nonlinear and high-dimensional limit state functions. A new active learning function that can be directly linked to the global error is first constructed. Weighting coefficients of the joint probability density function in the new active learning function are then determined to select the most probable points (MPPs) and update samples efficiently and accurately. The Gibbs importance sampling algorithm is derived based on the Gibbs algorithm to effectively establish the candidate importance sample pool. An improved global error-based stopping criterion is finally constructed to avoid pre-mature or late-mature for the estimation of small failure probabilities with complicated failure domains. Two numerical and four engineering examples are respectively employed to elaborate and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. A Novel Reliability Analysis Approach With Collaborative Active Learning Strategy-Based Augmented RBF Metamodel
- Author
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Yanxu Wei, Guangchen Bai, and Lu-Kai Song
- Subjects
Active learning function ,radial basis function ,reliability analysis ,metamodel ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Metamodels in lieu of time-demanding performance functions can accelerate the reliability analysis effectively. In this paper, we propose an efficient collaborative active learning strategy-based augmented radial basis function metamodel (CAL-ARBF), for reliability analysis with implicit and nonlinear performance functions. For generating the suitable samples, a CAL function is first designed to constrain the new samples being generated in sensitivity region, near limit state surface and keep certain distances mutually. Then by adjusting the adjustment coefficient of CAL function, the CAL-ARBF is mathematically modeled and the corresponding reliability analysis theory is developed. The effectiveness of the proposed approach is validated by four numerical samples, including global nonlinear problem, local nonlinear problem, nonlinear oscillator and truss structure. Through comparison of several state-of-the-art methods, the proposed CAL-ARBF is demonstrated to possess the computational advantages in efficiency and accuracy for reliability analysis.
- Published
- 2020
- Full Text
- View/download PDF
5. A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model
- Author
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Pengpeng Zhi, Guoli Yun, Zhonglai Wang, Peijing Shi, Xinkai Guo, Jiang Wu, and Zhao Ma
- Subjects
multiple response Gaussian process ,subset simulation ,reliability analysis ,active learning function ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method.
- Published
- 2022
- Full Text
- View/download PDF
6. 基于PC-Kriging模型与主动学习的齿轮热传递误差可靠性分析.
- Author
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于震梁, 孙志礼, 曹汝男, and 张毅博
- Subjects
- *
MONTE Carlo method , *MACHINE learning , *AKAIKE information criterion , *HEAT transfer , *KRIGING , *RELIABILITY in engineering - Abstract
To improve the computational efficiency and accuracy in the reliability analysis of gear heat transfer error, an efficient reliability analysis method combining PC-Kriging and active learning function LIF is proposed. Polynomial-chaos-expansion (PCE) is adopted to replace the regression basis function of the traditional Kriging model to enhance its global approximation accuracy and its ability to capture local features. The least-angle regression (LAR) is used to construct the optimal polynomial quantity set of the regression basis function, and the Akaike information criterion (AIC) is utilized to determine the optimal truncated set. Furthermore, the active learning function LIF is employed to select the optimal sample during each iteration to improve the convergence efficiency of the PC-Kriging model. The application to gear heat transfer error shows that compared with the traditional Kriging model, the proposed method can significantly reduce the number of performance function evaluations while ensuring accuracy in the reliability analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
7. An active learning reliability method with multiple kernel functions based on radial basis function.
- Author
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Shi, Lingjian, Sun, Beibei, and Ibrahim, Dauda Sh.
- Subjects
- *
ACTIVE learning , *MONTE Carlo method , *KERNEL functions , *RADIAL basis functions , *PROBLEM solving - Abstract
Surrogate models combined with Monte Carlo simulation (MCS) are effective ways to address failure probability problems, which involve time-consuming computer codes, in structural reliability analysis. Recently, many active learning functions based on the Kriging model have been developed to conduct adaptive sequential sampling due to its predicted value and variance. However, effective methods for learning functions based on other surrogate models to address failure probability problems remain sparse. Hence, this paper presents a new adaptive sampling function that combines the radial basis function (RBF) approximate model with MCS to calculate the failure probability of structures. The new learning function is named the active RBF method with multiple kernel functions and Monte Carlo simulation (ARBFM-MCS). It uses several RBF kernel functions to estimate the local uncertainty of the predicted values based on interquartile range (IQR) to formulate the active learning function. This method can establish several surrogate models simultaneously for application to different problems. The stopping criterion of this method is that if one of the errors calculated by the various RBF models meets the demand, the learning process stops automatically. Furthermore, the method is also compared with the k-fold cross-validation approach based on a single RBF kernel function and some other approaches presented in the literature. Six numerical examples are considered to verify the accuracy and the efficiency of the proposed method. The results reveal that the multiple kernel function method is effective and has the same accuracy level as other methods. Moreover, for these examples, the method often calls the limit state function in fewer times to obtain accurate failure probabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
8. Tunnel face reliability analysis using active learning Kriging model—Case of a two-layer soils.
- Author
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Li, Tian-zheng and Dias, Daniel
- Abstract
Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2019
- Full Text
- View/download PDF
9. A Novel Reliability Analysis Approach With Collaborative Active Learning Strategy-Based Augmented RBF Metamodel
- Author
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Guang-Chen Bai, Lu-Kai Song, and Yanxu Wei
- Subjects
metamodel ,Mathematical optimization ,General Computer Science ,Computer science ,Active learning (machine learning) ,General Engineering ,Truss ,020101 civil engineering ,02 engineering and technology ,Function (mathematics) ,0201 civil engineering ,Metamodeling ,Nonlinear system ,020303 mechanical engineering & transports ,0203 mechanical engineering ,Active learning function ,reliability analysis ,General Materials Science ,Limit state design ,Radial basis function ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Sensitivity (control systems) ,radial basis function ,lcsh:TK1-9971 ,Reliability (statistics) - Abstract
Metamodels in lieu of time-demanding performance functions can accelerate the reliability analysis effectively. In this paper, we propose an efficient collaborative active learning strategy-based augmented radial basis function metamodel (CAL-ARBF), for reliability analysis with implicit and nonlinear performance functions. For generating the suitable samples, a CAL function is first designed to constrain the new samples being generated in sensitivity region, near limit state surface and keep certain distances mutually. Then by adjusting the adjustment coefficient of CAL function, the CAL-ARBF is mathematically modeled and the corresponding reliability analysis theory is developed. The effectiveness of the proposed approach is validated by four numerical samples, including global nonlinear problem, local nonlinear problem, nonlinear oscillator and truss structure. Through comparison of several state-of-the-art methods, the proposed CAL-ARBF is demonstrated to possess the computational advantages in efficiency and accuracy for reliability analysis.
- Published
- 2020
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