Siam-Asa Journal on Uncertainty Quantification最新文献_第6页

Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems 旋转圆盘系统时空热通量贝叶斯推理的深度代理加速延迟接受哈密顿蒙特卡罗

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-08-28 DOI: 10.1137/22m1513113

Teo Deveney, Eike H. Mueller, T. Shardlow

引用次数: 0

A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity 用复杂度接近最优的多层蒙特卡罗方法对协方差函数的一种简单、无偏差逼近

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-08-23 DOI: 10.1137/22m1506845

A. Chernov, Erik Marc Schetzke

引用次数: 0

Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method 基于锚定方差分析Petrov-Galerkin方法的不确定鲁棒水平集拓扑优化

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-07-25 DOI: 10.1137/22m1524722

Christophe Audouze, Aaron E. Klein, Adrian Butscher, Nigel Morris, P. Nair, M. Yano

引用次数: 0

Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions 基于大偏差理论的高维罕见事件自适应重要性抽样

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-07-11 DOI: 10.1137/22m1524758

Shanyin Tong, Georg Stadler

{"title":"Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions","authors":"Shanyin Tong, Georg Stadler","doi":"10.1137/22m1524758","DOIUrl":"https://doi.org/10.1137/22m1524758","url":null,"abstract":"SIAM/ASA Journal on Uncertainty Quantification, Volume 11, Issue 3, Page 788-813, September 2023. <br/> Abstract. We propose a method for the accurate estimation of rare event or failure probabilities for expensive-to-evaluate numerical models in high dimensions. The proposed approach combines ideas from large deviation theory and adaptive importance sampling. The importance sampler uses a cross-entropy method to find an optimal Gaussian biasing distribution, and reuses all samples made throughout the process for both the target probability estimation and for updating the biasing distributions. Large deviation theory is used to find a good initial biasing distribution through the solution of an optimization problem. Additionally, it is used to identify a low-dimensional subspace that is most informative of the rare event probability. This subspace is used for the cross-entropy method, which is known to lose efficiency in higher dimensions. The proposed method does not require smoothing of indicator functions nor does it involve numerical tuning parameters. We compare the method with a state-of-the-art cross-entropy-based importance sampling scheme using three examples: a high-dimensional failure probability estimation benchmark, a problem governed by a diffusion equation, and a tsunami problem governed by the time-dependent shallow water system in one spatial dimension.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":"55 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling 基于集成梯度推理的粒子优化和采样方法

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-07-10 DOI: 10.1137/22m1533281

Claudia Schillings, Claudia Totzeck, Philipp Wacker

{"title":"Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling","authors":"Claudia Schillings, Claudia Totzeck, Philipp Wacker","doi":"10.1137/22m1533281","DOIUrl":"https://doi.org/10.1137/22m1533281","url":null,"abstract":"SIAM/ASA Journal on Uncertainty Quantification, Volume 11, Issue 3, Page 757-787, September 2023. <br/> Abstract. We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions from a given ensemble of particles. Pointwise evaluation of some potential V in an ensemble contains implicit information about first- or higher-order derivatives, which can be made explicit with little computational effort (ensemble-based gradient inference). We suggest using this information for the improvement of established ensemble-based numerical methods for optimization and sampling such as consensus-based optimization and Langevin-based samplers. Numerical studies indicate that the augmented algorithms are often superior to their gradient-free variants; in particular, the augmented methods help the ensembles to escape their initial domain, to explore multimodal, non-Gaussian settings, and to speed up the collapse at the end of optimization dynamics. The code for the numerical examples in this manuscript can be found in the paper’s Github repository.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":"14 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices 基于层次矩阵的可扩展物理最大似然估计

3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-06-05 DOI: 10.1137/21m1458880

Yian Chen, Mihai Anitescu

{"title":"Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices","authors":"Yian Chen, Mihai Anitescu","doi":"10.1137/21m1458880","DOIUrl":"https://doi.org/10.1137/21m1458880","url":null,"abstract":"Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unknown parameters in the covariance models can be estimated using maximum likelihood estimation, but direct construction of the covariance matrix and classical strategies of computing with it require physical model runs, storage complexity, and computational complexity. To address such challenges, we propose to approximate the discretized covariance function using hierarchical matrices. By utilizing randomized range sketching for individual off-diagonal blocks, the construction process of the hierarchical covariance approximation requires physical model applications and the maximum likelihood computations require effort per iteration. We propose a new approach to compute exactly the trace of products of hierarchical matrices which results in the expected Fisher information matrix being computable in as well. The construction is totally matrix-free and the derivatives of the covariance matrix can then be approximated in the same hierarchical structure by differentiating the whole process. Numerical results are provided to demonstrate the effectiveness, accuracy, and efficiency of the proposed method for parameter estimations and uncertainty quantification.","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135703572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise 无约束白噪声下一般线性逆问题的无噪声正则化

3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-05-25 DOI: 10.1137/22m1506675

Tim Jahn

引用次数: 0

Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification 不确定量化条件下Helmholtz解的波数显式参数全纯性

3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-05-18 DOI: 10.1137/22m1486170

E. A. Spence, J. Wunsch

{"title":"Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification","authors":"E. A. Spence, J. Wunsch","doi":"10.1137/22m1486170","DOIUrl":"https://doi.org/10.1137/22m1486170","url":null,"abstract":"A crucial role in the theory of uncertainty quantification (UQ) of PDEs is played by the regularity of the solution with respect to the stochastic parameters; indeed, a key property one seeks to establish is that the solution is holomorphic with respect to (the complex extensions of) the parameters. In the context of UQ for the high-frequency Helmholtz equation, a natural question is therefore: how does this parametric holomorphy depend on the wavenumber ? The recent paper [35] showed for a particular nontrapping variable-coefficient Helmholtz problem with affine dependence of the coefficients on the stochastic parameters that the solution operator can be analytically continued a distance into the complex plane. In this paper, we generalize the result in [35] about -explicit parametric holomorphy to a much wider class of Helmholtz problems with arbitrary (holomorphic) dependence on the stochastic parameters; we show that in all cases the region of parametric holomorphy decreases with and show how the rate of decrease with is dictated by whether the unperturbed Helmholtz problem is trapping or nontrapping. We then give examples of both trapping and nontrapping problems where these bounds on the rate of decrease with of the region of parametric holomorphy are sharp, with the trapping examples coming from the recent results of [31]. An immediate implication of these results is that the -dependent restrictions imposed on the randomness in the analysis of quasi-Monte Carlo methods in [35] arise from a genuine feature of the Helmholtz equation with large (and not, for example, a suboptimal bound).","PeriodicalId":56064,"journal":{"name":"Siam-Asa Journal on Uncertainty Quantification","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135717187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models 零问题:距离约束计算机模型的高斯过程仿真器

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-05-17 DOI: 10.1137/21m1467420

E. Spiller, R. Wolpert, Pablo Tierz, Taylor G. Asher

引用次数: 0

Nonparametric Posterior Learning for Emission Tomography 发射断层扫描的非参数后验学习

IF 2 3区工程技术

Siam-Asa Journal on Uncertainty Quantification Pub Date : 2023-05-11 DOI: 10.1137/21m1463367

F. Goncharov, E. Barat, T. Dautremer

引用次数: 0