Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 10.1615/int.j.uncertaintyquantification.2020031735
Pedro Bonilla-Villalba, S. Claus, A. Kundu, P. Kerfriden
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.
{"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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
Pub Date : 2020-01-01DOI: 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}
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