International Journal for Uncertainty Quantification最新文献_第8页

ACCRUE: ACCURATE AND RELIABLE UNCERTAINTY ESTIMATE IN DETERMINISTIC MODELS 累积:在确定性模型中准确可靠的不确定性估计

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021034623

E. Camporeale, A. Carè

引用次数: 7

SURROGATE BASED MUTUAL INFORMATION APPROXIMATION AND OPTIMIZATION FOR URBAN SOURCE LOCALIZATION 基于代理的城市源定位互信息逼近与优化

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021034400

A. Hollis, Ralph Smith, Alyson G. Wilson

引用次数: 0

Computational Challenges in Sampling and Representation of Uncertain Reaction Kinetics in Large Dimensions 大尺度不确定反应动力学的采样和表示中的计算挑战

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035691

Saja Almohammadi, O. Maître, O. Knio

{"title":"Computational Challenges in Sampling and Representation of Uncertain Reaction Kinetics in Large Dimensions","authors":"Saja Almohammadi, O. Maître, O. Knio","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035691","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035691","url":null,"abstract":"This work focuses on constructing functional representations of quantities of interest (QoIs) of an uncertain system in high dimension. Attention is focused on the ignition delay time of an iso-octane air mixture, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log-uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was ﬁrst generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parametrizing the uncertain rates. We explored two avenues, namely sparse regression (SR) using LASSO, and a coordinate transform (CT) approach. Preconditioned variants of both approaches were also considered, namely using the logarithm of the ignition delay time as QoI. Both approaches resulted in representations of the ignition delay with similar representation errors. However, the CT approach was able to reproduce better the empirical distribution of the underlying LHS ensemble, and also preserved the positivity of the ignition delay time. When preconditioned representations were considered, however, similar performances were obtained using CT and SR representations. The results also revealed that both the CT and SR representations yield consistent global sensitivity estimates. The results were ﬁnally used to test a reduced dimension representation, and to outline potential extensions of the work.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531167","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

Sparse Gaussian process model with mixed covariance function for uncertainty quantification 不确定性量化的混合协方差函数稀疏高斯过程模型

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2021035851

Kai Cheng, Zhenzhou Lu, Sinan Xiao, S. Oladyshkin, W. Nowak

引用次数: 2

PARAMETER ESTIMATION OF STOCHASTIC CHAOTIC SYSTEMS 随机混沌系统的参数估计

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032807

R. Maraia, Sebastian Springer, H. Haario, J. Hakkarainen, E. Saksman

引用次数: 2

A FETI-DP based parallel algorithm for solving high dimensional stochastic PDEs using collocation 基于FETI-DP的高维随机偏微分方程并行配置求解算法

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2021038512

G. Ajith, D. Ghosh

引用次数: 0

ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA 基于时空数据的贝叶斯模型校正鲁棒重要抽样

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021033499

Kyle Neal, Benjamin Schroeder, Joshua Mullins, Abhinav Subramanian, S. Mahadevan

引用次数: 1

A NON-NESTED INFILLING STRATEGY FOR MULTIFIDELITY BASED EFFICIENT GLOBAL OPTIMIZATION 基于多保真度的非嵌套填充策略的高效全局优化

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032982

M. Sacher, O. Maître, R. Duvigneau, F. Hauville, M. Durand, Corentin Lothodé

{"title":"A NON-NESTED INFILLING STRATEGY FOR MULTIFIDELITY BASED EFFICIENT GLOBAL OPTIMIZATION","authors":"M. Sacher, O. Maître, R. Duvigneau, F. Hauville, M. Durand, Corentin Lothodé","doi":"10.1615/int.j.uncertaintyquantification.2020032982","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020032982","url":null,"abstract":"Efﬁcient Global Optimization (EGO) has become a standard approach for the global optimization of complex systems with high computational costs. EGO uses a training set of objective function values computed at selected input points to construct a statistical surrogate model, with low evaluation cost, on which the optimization procedure is applied. The training set is sequentially enriched, selecting new points, according to a prescribed inﬁlling strategy, in order to converge to the optimum of the original costly model. Multi-ﬁdelity approaches combining evaluations of the quantity of interest at different ﬁdelity levels have been recently introduced to reduce the computational cost of building a global surrogate model. However, the use of multi-ﬁdelity approaches in the context of EGO is still a research topic. In this work, we propose a new effective inﬁlling strategy for multi-ﬁdelity EGO. Our inﬁlling strategy has the particularity of relying on non-nested training sets, a characteristic that comes with several computational beneﬁts. For the enrichment of the multi-ﬁdelity training set, the strategy selects the next input point together with the ﬁdelity level of the objective function evaluation. This characteristic is in contrast with previous nested approaches, which require estimation all lower ﬁdelity levels and are more demanding to update the surrogate. The resulting EGO procedure achieves a signiﬁcantly reduced computational cost, avoiding computations at useless ﬁdelity levels whenever possible, but it is also more robust to low correlations between levels and noisy estimations. Analytical problems are used to test and illustrate the efﬁciency of the method. It is ﬁnally applied to the optimization of a fully nonlinear ﬂuid-structure interaction system to demonstrate its feasibility on real large-scale problems, with ﬁdelity levels mixing physical approximations in the constitutive models and discretization reﬁnements.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530613","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

COMPUTATION OF SOBOL INDICES IN GLOBAL SENSITIVITY ANALYSIS FROM SMALL DATA SETS BY PROBABILISTIC LEARNING ON MANIFOLDS 基于流形概率学习的小数据集全局敏感性分析sobol指标计算

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/int.j.uncertaintyquantification.2020032674

M. Arnst, Christian Soize, K. Bulthuis

{"title":"COMPUTATION OF SOBOL INDICES IN GLOBAL SENSITIVITY ANALYSIS FROM SMALL DATA SETS BY PROBABILISTIC LEARNING ON MANIFOLDS","authors":"M. Arnst, Christian Soize, K. Bulthuis","doi":"10.1615/int.j.uncertaintyquantification.2020032674","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020032674","url":null,"abstract":"Global sensitivity analysis provides insight into how sources of uncertainty contribute to uncertainty in predictions of computational models. Global sensitivity indices, also called variance-based sensitivity indices and Sobol indices, are most often computed with Monte Carlo methods. However, when the computational model is computationally expensive and only a small number of samples can be generated, that is, in so-called small-data settings, usual Monte Carlo estimates may lack sufficient accuracy. As a means of improving accuracy in such small-data settings, we explore the use of probabilistic learning. The objective of the probabilistic learning is to learn from the available samples a probabilistic model that can be used to generate additional samples, from which Monte Carlo estimates of the global sensitivity indices are then deduced. We demonstrate the interest of such a probabilistic learning method by applying it in an illustration concerned with forecasting the contribution of the Antarctic ice sheet to sea-level rise.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530892","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: 10

EXPLORATION OF MULTIFIDELITY UQ SAMPLING STRATEGIES FOR COMPUTER NETWORK APPLICATIONS 计算机网络应用中多保真度uq采样策略的探索

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2021-01-01 DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021033774

G. Geraci, J. Crussell, L. Swiler, B. Debusschere

引用次数: 2