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International Journal for Uncertainty Quantification最新文献

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ISOGEOMETRIC METHODS FOR KARHUNEN-LOEVE REPRESENTATION OF RANDOM FIELDS ON ARBITRARY MULTIPATCH DOMAINS 任意多斑域上随机场karhunen-loeve表示的等几何方法
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020035185
Ramin Jahanbin, S. Rahman
{"title":"ISOGEOMETRIC METHODS FOR KARHUNEN-LOEVE REPRESENTATION OF RANDOM FIELDS ON ARBITRARY MULTIPATCH DOMAINS","authors":"Ramin Jahanbin, S. Rahman","doi":"10.1615/int.j.uncertaintyquantification.2020035185","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020035185","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
GOAL-ORIENTED MODEL ADAPTIVITY IN STOCHASTIC ELASTODYNAMICS: SIMULTANEOUS CONTROL OF DISCRETIZATION, SURROGATE MODEL AND SAMPLING ERRORS 随机弹性动力学中目标导向模型的自适应:离散化、代理模型和抽样误差的同时控制
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020031735
Pedro Bonilla-Villalba, S. Claus, A. Kundu, P. Kerfriden
{"title":"GOAL-ORIENTED MODEL ADAPTIVITY IN STOCHASTIC ELASTODYNAMICS: SIMULTANEOUS CONTROL OF DISCRETIZATION, SURROGATE MODEL AND SAMPLING ERRORS","authors":"Pedro Bonilla-Villalba, S. Claus, A. Kundu, P. Kerfriden","doi":"10.1615/int.j.uncertaintyquantification.2020031735","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020031735","url":null,"abstract":"The presented adaptive modelling approach aims to jointly control the level of renement for each of the building-blocks employed in a typical chain of nite element approximations for stochastically parametrized systems, namely: (i) nite error approximation of the spatial elds (ii) surrogate modelling to interpolate quantities of interest(s) in the parameter domain and (iii) Monte-Carlo sampling of associated probability distribution(s). The control strategy seeks accurate calculation of any statistical measure of the distributions at minimum cost, given an acceptable margin of error as only tunable parameter. At each stage of the greedy-based algorithm for spatial discretisation, the mesh is selectively rened in the subdomains with highest contribution to the error in the desired measure. The strictly incremental complexity of the surrogate model is controlled by enforcing preponderant discretisation error integrated across the parameter domain. Finally, the number of Monte-Carlo samples is chosen such that either (a) the overall precision of the chain of approximations can be ascertained with sucient condence, or (b) the fact that the computational model requires further mesh renement is statistically established. The eciency of the proposed approach is discussed for a frequency-domain vibration structural dynamics problem with forward uncertainty propagation. Results show that locally adapted nite element solutions converge faster than those obtained using uniformly rened grids.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
UNCERTAINTY QUANTIFICATION OF DETONATION THROUGH ADAPTED POLYNOMIAL CHAOS 用自适应多项式混沌定量爆轰的不确定度
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020030630
Xiao Liang, Ruili Wang, R. Ghanem
{"title":"UNCERTAINTY QUANTIFICATION OF DETONATION THROUGH ADAPTED POLYNOMIAL CHAOS","authors":"Xiao Liang, Ruili Wang, R. Ghanem","doi":"10.1615/int.j.uncertaintyquantification.2020030630","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020030630","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":"83-100"},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
SENSITIVITY ANALYSIS FOR STOCHASTIC SIMULATORS USING DIFFERENTIAL ENTROPY 基于微分熵的随机模拟器灵敏度分析
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020031610
S. Azzi, B. Sudret, J. Wiart
{"title":"SENSITIVITY ANALYSIS FOR STOCHASTIC SIMULATORS USING DIFFERENTIAL ENTROPY","authors":"S. Azzi, B. Sudret, J. Wiart","doi":"10.1615/int.j.uncertaintyquantification.2020031610","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020031610","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":"25-33"},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
MULTIFIDELITY MODELING OF IRRADIATED PARTICLE-LADEN TURBULENCE SUBJECT TO UNCERTAINTY 受不确定辐射粒子负载湍流的多保真度建模
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032236
L. Jofre, Manolis Papadakis, P. Roy, A. Aiken, G. Iaccarino
{"title":"MULTIFIDELITY MODELING OF IRRADIATED PARTICLE-LADEN TURBULENCE SUBJECT TO UNCERTAINTY","authors":"L. Jofre, Manolis Papadakis, P. Roy, A. Aiken, G. Iaccarino","doi":"10.1615/int.j.uncertaintyquantification.2020032236","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020032236","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":"499-514"},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
MULTILEVEL MONTE CARLO SAMPLING ON HETEROGENEOUS COMPUTER ARCHITECTURES 异构计算机体系结构上的多级蒙特卡罗采样
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020033179
C. Adcock, Y. Ye, L. Jofre, G. Iaccarino
{"title":"MULTILEVEL MONTE CARLO SAMPLING ON HETEROGENEOUS COMPUTER ARCHITECTURES","authors":"C. Adcock, Y. Ye, L. Jofre, G. Iaccarino","doi":"10.1615/int.j.uncertaintyquantification.2020033179","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020033179","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
DATA-DRIVEN CALIBRATION OF P3D HYDRAULIC FRACTURING MODELS 数据驱动的p3d水力压裂模型标定
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020033602
S. Zio, F. Rochinha
{"title":"DATA-DRIVEN CALIBRATION OF P3D HYDRAULIC FRACTURING MODELS","authors":"S. Zio, F. Rochinha","doi":"10.1615/int.j.uncertaintyquantification.2020033602","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020033602","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":"375-398"},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
ON THE MULTILEVEL MONTE CARLO ESTIMATION OF UNBIASED EXPECTATION VIA SEQUENCE EXTRAPOLATION 用序列外推法研究无偏期望的多水平蒙特卡罗估计
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032985
T. Barth
{"title":"ON THE MULTILEVEL MONTE CARLO ESTIMATION OF UNBIASED EXPECTATION VIA SEQUENCE EXTRAPOLATION","authors":"T. Barth","doi":"10.1615/int.j.uncertaintyquantification.2020032985","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020032985","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DATA-CONSISTENT SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING A PROBABILISTIC MULTI-FIDELITY METHOD BASED ON CONDITIONAL DENSITIES 基于条件密度的概率多保真度方法的随机逆问题数据一致解
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2020030092
L. Bruder, M. W. Gee, T. Wildey
{"title":"DATA-CONSISTENT SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING A PROBABILISTIC MULTI-FIDELITY METHOD BASED ON CONDITIONAL DENSITIES","authors":"L. Bruder, M. W. Gee, T. Wildey","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2020030092","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2020030092","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
MODEL CALIBRATION FOR DETONATION PRODUCTS: A PHYSICS-INFORMED, TIME-DEPENDENT SURROGATE METHOD BASED ON MACHINE LEARNING 爆炸产品的模型校准:基于机器学习的物理信息，时间依赖的替代方法
IF 1.7 4区 工程技术
International Journal for Uncertainty Quantification Pub Date : 2020-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032977
Juan Zhang, J. Yin, Ruili Wang, J. Chen
{"title":"MODEL CALIBRATION FOR DETONATION PRODUCTS: A PHYSICS-INFORMED, TIME-DEPENDENT SURROGATE METHOD BASED ON MACHINE LEARNING","authors":"Juan Zhang, J. Yin, Ruili Wang, J. Chen","doi":"10.1615/int.j.uncertaintyquantification.2020032977","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020032977","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"10 1","pages":"277-296"},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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