International Journal for Uncertainty Quantification最新文献_第2页

Derivative-based Shapley value for global sensitivity analysis and machine learning explainability 基于衍生的夏普利值，用于全局敏感性分析和机器学习的可解释性

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-05-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051548

Hui Duan, Giray Okten

引用次数: 0

Probabilistic Uncertainty Propagation Using Gaussian Process Surrogates 利用高斯过程代理进行概率不确定性传播

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-05-01 DOI: 10.1615/int.j.uncertaintyquantification.2024052162

Paolo Manfredi

{"title":"Probabilistic Uncertainty Propagation Using Gaussian Process Surrogates","authors":"Paolo Manfredi","doi":"10.1615/int.j.uncertaintyquantification.2024052162","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2024052162","url":null,"abstract":"This paper introduces a simple and computationally tractable probabilistic framework for forward uncertainty quantification based on Gaussian process regression, also known as Kriging. The aim is to equip uncertainty measures in the propagation of input uncertainty to simulator outputs with predictive uncertainty and confidence bounds accounting for the limited accuracy of the surrogate model, which is mainly due to using a finite amount of training data. The additional uncertainty related to the estimation of some of the prior model parameters (namely, trend coefficients and kernel variance) is further accounted for. Two different scenarios are considered. In the first one, the Gaussian process surrogate is used to emulate the actual simulator and propagate input uncertainty in the framework of a Monte Carlo analysis, i.e., as computationally cheap replacement of the original code. In the second one, semi-analytical estimates for the statistical moments of the output quantity are obtained directly based on their integral definition. The estimates for the first scenario are more general, more tractable, and they naturally extend to inputs of higher dimensions. The impact of noise on the target function is also discussed. Our findings are demonstrated based on a simple illustrative function and validated by means of several benchmark functions and a high-dimensional test case with more than a hundred uncertain variables.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"31 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189356","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

PARALLEL PARTIAL EMULATION IN APPLICATIONS 应用中的并行局部仿真

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-05-01 DOI: 10.1615/int.j.uncertaintyquantification.2024048538

Yingjie Gao, E Bruce Pitman

{"title":"PARALLEL PARTIAL EMULATION IN APPLICATIONS","authors":"Yingjie Gao, E Bruce Pitman","doi":"10.1615/int.j.uncertaintyquantification.2024048538","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2024048538","url":null,"abstract":"Emulators are used to approximate the output of large computer simulations.\u0000Statistical emulators are surrogates that, in addition to predicting the mean behavior of the system, provide an estimate of the error in that prediction.\u0000Classical Gaussian Stochastic Process emulators predict scalar outputs based on a modest number of input parameters.\u0000To make predictions across a space-time field of input variables is not feasible using classical Gaussian process methods.\u0000Parallel Partial Emulation is a new statistical emulator methodology that predicts a field of outputs at space-time locations, based on a set of input parameters of modest dimension.\u0000Parallel partial emulation is constructed as a Gaussian process in parameter space, but no correlation in space/time is assumed. Thus the computational work of parallel partial emulation scales as the cube of the number of input parameters (as traditional Gaussian Process emulation) and linearly with space-time grid.\u0000The behavior of Parallel Partial Emulation predictions in complex applications is not well understood.\u0000Scientists would like to understand how predictions depend on the separation of input parameters, across the space-time outputs.\u0000It is also of interest to study whether the emulator predictions inherit properties (e.g conservation) from the numerical simulator.\u0000This paper studies the properties of emulator predictions, in the context of scalar and systems of partial differential equation.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889294","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

Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors 最大化区域敏感性分析指数，找到敏感的模型行为

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-04-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051424

Sebastien Roux, Patrice Loisel, Samuel Buis

引用次数: 0

Structure-Preserving Model Order Reduction of Random Parametric Linear Systems via Regression 通过回归减少随机参数线性系统的结构保持模型阶次

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-04-01 DOI: 10.1615/int.j.uncertaintyquantification.2024048898

Xiaolong Wang, Siqing Liu

{"title":"Structure-Preserving Model Order Reduction of Random Parametric Linear Systems via Regression","authors":"Xiaolong Wang, Siqing Liu","doi":"10.1615/int.j.uncertaintyquantification.2024048898","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2024048898","url":null,"abstract":"We investigate model order reduction (MOR) of random parametric linear systems via the regression method. By sampling the random parameters containing in the coefficient matrices of the systems via Latin hypercube method, the iterative rational Krylov algorithm (IRKA) is used to generate sample reduced models corresponding to the sample data. We assemble the resulting reduced models by interpolating the coefficient matrices of reduced sample models with the regression technique, where the generalized polynomial chaos (gPC) are adopted to characterize the random dependence coming from the original systems. Noting the invariance of the transfer function with respect to restricted equivalence transformations, the regression method is conducted based on the controllable canonical form of reduced sample models in such a way to improve the accuracy of reduced models greatly. We also provide a posteriori error bound for the projection reduction method in the stochastic setting. We showcase the efficiency of the proposed approach by two large-scale systems along with random parameters: a synthetic model and a mass-spring-damper system.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"48 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634987","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

SOLVING STOCHASTIC INVERSE PROBLEMS FOR CFD USING DATA-CONSISTENT INVERSION AND AN ADAPTIVE STOCHASTIC COLLOCATION METHOD 利用数据一致反演和自适应随机搭配法解决 cfd 的随机逆问题

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-04-01 DOI: 10.1615/int.j.uncertaintyquantification.2024049566

Hector Galante, Anca Belme, Jean-Camille Chassaing, Timothy Wildey

{"title":"SOLVING STOCHASTIC INVERSE PROBLEMS FOR CFD USING DATA-CONSISTENT INVERSION AND AN ADAPTIVE STOCHASTIC COLLOCATION METHOD","authors":"Hector Galante, Anca Belme, Jean-Camille Chassaing, Timothy Wildey","doi":"10.1615/int.j.uncertaintyquantification.2024049566","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2024049566","url":null,"abstract":"We present a non-intrusive adaptive stochastic collocation method coupled with a data-consistent inference framework to optimize stochastic inverse problems solve in CFD. The purpose of the proposed data-consistent method is, given a model and some observed output probability density function (pdf), to build a new model input pdf which is consistent with both the model and the data. Solving stochastic inverse problems in CFD is however very costly, which is why we use a surrogate or metamodel in the data-consistent inference method. This surrogate model is built using an adaptive stochastic collocation approach based on a stochastic error estimator and simplex elements in the parameters space. The efficiency of the proposed method is evaluated on analytical test cases and two CFD configurations. The metamodel inference results are shown to be as accurate as crude Monte Carlo inferences while performing 103 less deterministic computations for smooth and discontinuous response surfaces. Moreover, the proposed method is shown to be able to reconstruct both an observed pdf on the data and a data-generating distribution in the uncertain parameter space under certain conditions.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"28 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627318","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 Analysis for Drift-Diffusion Equations 漂移-扩散方程的不确定性分析

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-04-01 DOI: 10.1615/int.j.uncertaintyquantification.2024039459

Greta Marino, Jan-Frederik Pietschmann, Alois Pichler

引用次数: 0

Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models 为非确定性模型开发基于样本的多保真度估计器的挑战分析

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-03-01 DOI: 10.1615/int.j.uncertaintyquantification.2024050125

Bryan Reuter, Gianluca Geraci, Timothy Wildey

{"title":"Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models","authors":"Bryan Reuter, Gianluca Geraci, Timothy Wildey","doi":"10.1615/int.j.uncertaintyquantification.2024050125","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2024050125","url":null,"abstract":"Multifidelity (MF) Uncertainty Quantification (UQ) seeks to leverage and fuse information from a collection of models to achieve greater statistical accuracy with respect to a single-fidelity counterpart, while maintaining an efficient use of computational resources.\u0000Despite many recent advancements in MF UQ, several challenges remain and these often limit its practical impact in certain application areas. In this manuscript, we focus on the challenges introduced by non-deterministic models to sampling MF UQ estimators.\u0000Non-deterministic models produce different responses for the same inputs, which means their outputs are effectively noisy. MF UQ becomes complicated by this noise since many state-of-the-art approaches rely on statistics, e.g., the correlation among models, to optimally fuse information and allocate computational resources. We demonstrate how the statistics of the quantities of interest, which impact the design, effectiveness, and use of existing MF UQ techniques, change as functions of the noise. With this in hand, we extend the unifying Approximate Control Variate framework to account for non-determinism, providing for the first time a rigorous means of comparing the effect of non-determinism on different multifidelity estimators and analyzing their performance with respect to one another. Numerical examples are presented throughout the manuscript to illustrate and discuss the consequences of the presented theoretical results.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"116 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108115","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

EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS 基于方差的全局敏感性分析的极端学习机

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-03-01 DOI: 10.1615/int.j.uncertaintyquantification.2024049519

John Darges, Alen Alexanderian, Pierre Gremaud

引用次数: 0

Measuring inputs-outputs association for time-depending hazard models under safety objectives using kernels 利用核素测量安全目标下随时间变化的危险模型的投入产出关联性

IF 1.7 4区工程技术

International Journal for Uncertainty Quantification Pub Date : 2024-03-01 DOI: 10.1615/int.j.uncertaintyquantification.2024049119

Matieyendou LAMBONI

引用次数: 0