Jingwen Song

Jingwen Song
Northwestern Polytechnical University | NWPU · School of Mechanical Engineering

Doctor of Philosophy

About

31
Publications
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711
Citations

Publications

Publications (31)
Conference Paper
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Probabilistic engineering computation involves two groups of models, i.e., the probability model for characterizing the randomness of input variables, and the physic model (usually described as a set of PDEs) for describing the behavior of a physic system. The probabilistic analysis aims at propagating the probability models through the physic one,...
Article
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Imprecise probabilities have gained increasing popularity for quantitatively modelling uncertainty under incomplete information in various fields. However, it is still a computationally challenging task to propagate imprecise probabilities since a double-loop procedure is usually involved. In this contribution, a fully decoupled method, termed as `...
Article
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Variance-based sensitivity indices play an important role in scientific computation and data mining, thus the significance of developing numerical methods for efficient and reliable estimation of these sensitivity indices based on (expensive) computer simulators and/or data cannot be emphasized too much. In this article, the estimation of these sen...
Article
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This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic...
Article
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Line Sampling (LS) has been widely recognized as one of the most appealing stochastic simulation algorithms for rare event analysis, but when applying it to many real-world engineering problems, improvement of the algorithm with higher efficiency is still required. This paper aims to improve both the efficiency and accuracy of LS by active learning...
Article
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Published version available at https://authors.elsevier.com/a/1bcOfAQEIt1QG. The efficient estimation of the failure probability function of rare failure events is a challenging task in the structural safety analysis when the input variables are characterized by imprecise probability models due to insufficient information on these variables. The re...
Thesis
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Uncertainty quantification (UQ) has been widely recognized as one of the most important, yet challenging task in both structural engineering and system engineering, and the current researches are mainly on the proper treatment of different types of uncertainties, resulting from either natural randomness or lack of information, in all related sub-pr...
Article
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The non-intrusive imprecise stochastic simulation (NISS) is a general framework for the propagation of imprecise probability models and analysis of reliability. The most appealing character of this methodology framework is that, being a pure simulation method, only one precise stochastic simulation is needed for implementing the method, and the req...
Presentation
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Invited presentation given on CIVIL-COMP 2019, 16-19 September 2019 | Riva del Garda, near Lake Garda, Italy.
Conference Paper
Uncertainty characterization and propagation through computational models are the two key basic problems in risk and reliability analysis of structures and systems. Commonly used methods are mostly based on precise probability models, which are effective for characterizing the aleatory uncertainty. In real-world applications, the available data of...
Article
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Non-intrusive Imprecise Stochastic Simulation (NISS) is a recently developed general methodological framework for efficiently propagating the imprecise probability models and for estimating the resultant failure probability functions and bounds. Due to the simplicity, high efficiency, stability and good convergence, it has been proved to be one of...
Data
If you have any questions on either methods or codes, please feel free to contact me at pengfeiwei@nwpu.edu.cn. This package provides Matlab codes of the "Non-intrusive imprecise stochastic simulation (NISS)" methodology framework for efficiently propagating the imprecise probability models. The codes are developed for the methods developed in the...
Data
If you have any questions on either methods or codes, please feel free to contact me at pengfeiwei@nwpu.edu.cn. This package provides Matlab codes of the "Non-intrusive imprecise stochastic simulation (NISS)" methodology framework for efficiently propagating the imprecise probability models. The codes are developed for the methods developed in the...
Article
Full-text available
(All the codes are attached. Please feel free to download and use them. If you have any question, don't hesitate to contact me.) Uncertainty propagation through the simulation models is critical for computational mechanics engineering to provide robust and reliable design in the presence of polymorphic uncertainty. This set of companion papers pres...
Chapter
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This is a draft version of an invited chapter which has been published in the Chinese book"李杰, 陈建兵, 彭永波. 随机振动理论与应用新进展(第Ⅱ辑). 上海:同济大学出版社, 2018". It focuses on reviewing the recent developments on uncertainty quantification models and sensitivity analysis under uncertainty environment. Unfortunately, only Chinese version is available. 本文是关于不确定性量化模型和敏感...
Article
Full-text available
(All the codes are attached. Please feel free to download and use them. If you have any question, don't hesitate to contact me.) Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stoc...
Article
In many disciplines involving high-dimensional data, permutation variable importance measure (PVIM) based on random forest is widely used for importance ranking of model inputs. This work extends the traditional PVIM to investigate the regional effects of the internal value range of model inputs. The PVIM function is firstly defined to measure the...
Article
The kinematic failure of a mechanism is commonly caused by the random input errors such as the errors of component dimensions and motion inputs. For identifying the main source of the failure probability, the global reliability sensitivity analysis is introduced. The method is based on decomposing the variance of the failure domain indicator functi...
Article
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Permutation variable importance measure (PVIM) based on random forest and Morris' screening design are two effective techniques for measuring the variable importance in high dimensions. The former technique is developed in the machine learning discipline and widely used in bioinformatics, while the latter technique is popular in scientific computin...
Article
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Measuring variable importance for computational models or measured data is an important task in many applications. It has drawn our attention that the variable importance analysis (VIA) techniques were developed independently in many disciplines. We are strongly aware of the necessity to aggregate all the good practices in each discipline, and comp...
Article
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Nowadays, utilizing the Monte Carlo estimators for variance-based sensitivity analysis has gained sufficient popularity in many research fields. These estimators are usually based on n+2 sample matrices well designed for computing both the main and total effect indices, where n is the input dimension. The aim of this paper is to use such n+2 sample...
Article
Global sensitivity analysis techniques for computational models with precise random inputs have been studied widely in the past few decades. However, in real engineering application, due to the lack of information, the distributions of input variables cannot be specified uniquely, and other models such as probability-box (p-box) need to be introduc...
Article
Nowadays, several importance analysis methods have been developed for model with correlated inputs. For choosing the most appropriate analysis methods to meet different requirements, it is necessary to make differences among these existing methods. In this paper, the importance indices, including the total, the structural and the correlative contri...
Article
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Estimating the functional relation between the probabilistic response of a computational model and the distribution parameters of the model inputs is especially useful for 1)assessing the contribution of the distribution parameters of model inputs to the uncertainty of model output (parametric global sensitivity analysis), and 2)identifying the opt...
Article
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The variance ratio function, derived from the contribution to sample variance (CSV) plot, is a regional sensitivity index for studying how much the output deviates from the original mean of model output when the distribution range of one input is reduced and to measure the contribution of different distribution ranges of each input to the variance...
Article
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A new set of variance-based sensitivity indices, called W-indices, is proposed. Similar to the Sobol's indices, both main and total effect indices are defined. The W-main effect indices measure the average reduction of model output variance when the ranges of a set of inputs are reduced, and the total effect indices quantify the average residual va...
Article
This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts effi...
Article
In risk assessment, the moment-independent sensitivity analysis (SA) technique for reducing the model uncertainty has attracted a great deal of attention from analysts and practitioners. It aims at measuring the relative importance of an individual input, or a set of inputs, in determining the uncertainty of model output by looking at the entire di...

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