International Journal for Uncertainty Quantification最新文献_第10页

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":null,"pages":null},"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

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

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

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

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

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

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

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

引用次数: 0

BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO. 随机反应网络的多保真顺序回火马尔可夫链蒙特卡罗贝叶斯推理。

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.2020033241

Thomas A Catanach, Huy D Vo, Brian Munsky

{"title":"BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.","authors":"Thomas A Catanach, Huy D Vo, Brian Munsky","doi":"10.1615/int.j.uncertaintyquantification.2020033241","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020033241","url":null,"abstract":"<p><p>Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic framework for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty. Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.</p>","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127724/pdf/nihms-1610699.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38926186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10