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Sobol’ sensitivity indices– A Machine Learning approach using the Dynamic Adaptive Variances Estimator with Given Data 索博尔敏感度指数--利用给定数据动态自适应方差估计器的机器学习方法
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-09-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051654
Ivano Azzini, Rossana Rosati
Global sensitivity analysis is today a widely recognized discipline with an extensive application in an increasing number of domains. Today, methodological development and available software, as well as a broader knowledge and debate on the topic, make investigations feasible which were simply impossible or too demanding a few years ago.Among global sensitivity methods, the variance-based techniques and Monte Carlo-based estimators related to Sobol’ sensitivity indices are mostly implemented due to their versatility and easiness of interpretation. Nevertheless, the strict dependency of the analysis cost on the number of the investigated factors and the need of a designed input are still a major issue.A reduction of the required model evaluations can be achieved with the use of quasi-Monte Carlo sequences, the study of groups of inputs, and the sensitivity indices computation through higher performing estimators such as the Innovative Algorithm based on dynamic adaptive variances recently proposed by the authors. However, all these strategies even cutting significantly the necessary model runs are not able to overcome the barrier of a structured input.This paper proposes a machine learning approach that allows us to estimate Sobol’ indices using the outstanding dynamic adaptive variances estimator starting from a set of Monte Carlo given data. Tests have been run on three relevant functions. In most cases, the results are very promising and seem to positively overcome the limit of a design-data approach keeping all the advantages of the Sobol’ Monte Carlo estimator.
如今,全局敏感性分析已成为一门广受认可的学科,在越来越多的领域得到广泛应用。在全局灵敏度方法中,基于方差的技术和基于蒙特卡罗的与索博尔灵敏度指数相关的估算器因其通用性和易于解释而被广泛采用。通过使用准蒙特卡罗序列、对输入组的研究以及通过性能更高的估计器(如作者最近提出的基于动态自适应方差的创新算法)计算灵敏度指数,可以减少所需的模型评估。本文提出了一种机器学习方法,使我们能够使用出色的动态自适应方差估计器,从一组蒙特卡罗给定数据开始估计索博尔指数。我们对三个相关函数进行了测试。在大多数情况下,测试结果都非常令人满意,而且似乎克服了设计数据方法的限制,保留了索博尔蒙特卡洛估计器的所有优点。
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引用次数: 0
Bayesian³ Active learning for regularized arbitrary multi-element polynomial chaos using information theory 利用信息论对正则化任意多元素多项式混沌进行贝叶斯³主动学习
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-09-01 DOI: 10.1615/int.j.uncertaintyquantification.2024052675
Ilja Kröker, Tim Brünnette, Nils Wildt, Maria Fernanda Morales Oreamuno, Rebecca Kohlhaas, Sergey Oladyshkin, Wolfgang Nowak
Machine learning, surrogate modeling, and uncertainty quantification pose challenges in data-poor applications that arise due to limited availability of measurement data or with computationally expensive models. Specialized models, derived from Gaussian process emulators (GPE) or polynomial chaos expansions (PCE), are often used when only limited amounts of training points are available. The PCE (or its data-driven version, the arbitrary polynomial chaos) is based on a global representation informed by the distributions of model parameters, whereas GPEs rely on a local kernel and additionally assess the uncertainty of the surrogate itself. Oscillation-mitigating localizations of the PCE result in increased degrees of freedom (DoF), requiring more training samples. As applications such as Bayesian inference (BI) require highly accurate surrogates, even specialized models like PCE or GPE require a substantial amount of training data. Bayesian³ active learning (B³AL) on GPEs, based on information theory (IT), can reduce the necessary number of training samples for BI. IT-based ideas for B³AL are not yet directly transferable to the PCE family, as this family lacks awareness of surrogate uncertainty by design. In the present work, we introduce a Bayesian regularized version of localized arbitrary polynomial chaos to build surrogate models. Equipped with Gaussian emulator properties, our fully adaptive framework is enhanced with B³AL methods designed to achieve reliable surrogate models for BI while efficiently selecting training samples via IT. The effectiveness of the proposed methodology is demonstrated by comprehensive evaluations on several numerical examples.
机器学习、代用建模和不确定性量化在数据匮乏的应用中面临着挑战,这是因为测量数据的可用性有限或模型的计算成本昂贵。当只有有限数量的训练点可用时,通常会使用从高斯过程模拟器(GPE)或多项式混沌展开(PCE)衍生出的专业模型。PCE(或其数据驱动版本,即任意多项式混沌)基于全局表示法,由模型参数的分布提供信息,而 GPE 则依赖于局部内核,并额外评估代用指标本身的不确定性。消除振荡的 PCE 局部化会增加自由度 (DoF),从而需要更多的训练样本。由于贝叶斯推理(BI)等应用需要高精度的代用指标,即使是像 PCE 或 GPE 这样的专业模型也需要大量的训练数据。基于信息论(IT)的 GPE 贝叶斯主动学习(B³AL)可以减少贝叶斯推理所需的训练样本数量。基于 IT 的 B³AL 理念还不能直接应用于 PCE 系列,因为该系列在设计上缺乏对代理不确定性的认识。在本研究中,我们引入了贝叶斯正则化版本的局部任意多项式混沌来建立代用模型。我们的全自适应性框架具有高斯仿真器特性,并采用 B³AL 方法进行了增强,旨在为 BI 建立可靠的代用模型,同时通过 IT 高效地选择训练样本。通过对几个数值示例的综合评估,证明了所提方法的有效性。
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引用次数: 0
A novel probabilistic transfer learning strategy for polynomial regression 用于多项式回归的新型概率转移学习策略
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-08-01 DOI: 10.1615/int.j.uncertaintyquantification.2024052051
Wyatt Bridgman, Uma Balakrishnan, Reese E. Jones, Jiefu Chen, Xuqing Wu, Cosmin Safta, Yueqin Huang, Mohammad Khalil
In the field of surrogate modeling and, more recently, with machine learning, transfer learning methodologies have been proposed in which knowledge from a source task is transferred to a target task where sparse and/or noisy data result in an ill-posed calibration problem. Such sparsity can result from prohibitively expensive forward model simulations or simply lack of data from experiments. Transfer learning attempts to improve target model calibration by leveraging similarities between the source and target tasks.This often takes the form of parameter-based transfer, which exploits correlations between the parameters defining the source and target models in order to regularize the target task. The majority of these approaches are deterministic and do not account for uncertainty in the model parameters. In this work, we propose a novel probabilistic transfer learning methodology which transfers knowledge from the posterior distribution of source to the target Bayesian inverse problem using an approach inspired by data assimilation.While the methodology is presented generally, it is subsequently investigated in the context of polynomial regression and, more specifically, Polynomial Chaos Expansions which result in Gaussian posterior distributions in the case of iid Gaussian observation noise and conjugate Gaussian prior distributions. The strategy is evaluated using numerical investigations and applied to an engineering problem from the oil and gas industry.
在代用建模领域,以及最近的机器学习领域,人们提出了迁移学习方法,将源任务中的知识迁移到目标任务中,在目标任务中,稀疏和/或嘈杂的数据会导致难以解决的校准问题。这种稀疏性可能源于昂贵的前向模型模拟,也可能仅仅是缺乏实验数据。迁移学习试图利用源任务和目标任务之间的相似性来改进目标模型的校准。这通常采取基于参数的迁移形式,即利用定义源模型和目标模型的参数之间的相关性来规范目标任务。这些方法大多是确定性的,不考虑模型参数的不确定性。在这项工作中,我们提出了一种新颖的概率转移学习方法,利用一种受数据同化启发的方法,将源后验分布中的知识转移到目标贝叶斯逆问题中。在对该方法进行一般性介绍的同时,我们随后在多项式回归的背景下对其进行了研究,更具体地说,多项式混沌展开(Polynomial Chaos Expansions)会在同位高斯观测噪声和共轭高斯先验分布的情况下产生高斯后验分布。通过数值研究对该策略进行了评估,并将其应用于石油和天然气行业的一个工程问题。
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引用次数: 0
Variance-based sensitivity of Bayesian inverse problems to the prior distribution 基于方差的贝叶斯逆问题对先验分布的敏感性
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-08-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051475
John Darges, Alen Alexanderian, Pierre Gremaud
The formulation of Bayesian inverse problems involves choosing priordistributions; choices that seem equally reasonable may lead to significantlydifferent conclusions. We develop a computational approach to betterunderstand the impact of the hyperparameters defining the prior on theposterior statistics of the quantities of interest. Our approach relies onglobal sensitivity analysis (GSA) of Bayesian inverse problems with respect tothe hyperparameters defining the prior. This, however, is a challengingproblem---a naive double loop sampling approach would require running a prohibitivenumber of Markov chain Monte Carlo (MCMC) sampling procedures. The presentwork takes a foundational step in making such a sensitivity analysis practicalthrough (i) a judicious combination of efficient surrogate models and (ii) atailored importance sampling method. In particular, we can perform accurateGSA of posterior prediction statistics with respect to prior hyperparameterswithout having to repeat MCMC runs. We demonstrate the effectiveness of theapproach on a simple Bayesian linear inverse problem and a nonlinear inverseproblem governed by an epidemiological model.
贝叶斯逆问题的提出涉及先验分布的选择;看似同样合理的选择可能会导致截然不同的结论。我们开发了一种计算方法,以更好地理解定义先验的超参数对相关量的后验统计的影响。我们的方法依赖于对定义先验的超参数进行贝叶斯逆问题的全局灵敏度分析(GSA)。然而,这是一个具有挑战性的问题--如果采用天真的双循环采样方法,则需要运行数量惊人的马尔可夫链蒙特卡罗(MCMC)采样程序。目前的研究工作迈出了奠基性的一步,通过(i)明智地结合高效代用模型和(ii)有针对性的重要度抽样方法,使这种敏感性分析切实可行。特别是,我们无需重复 MCMC 运行,就能对相对于先验超参数的后验预测统计量进行精确的 GSA。我们在一个简单的贝叶斯线性逆问题和一个由流行病学模型控制的非线性逆问题上演示了该方法的有效性。
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引用次数: 0
Extremes of vector-valued processes by finite dimensional models 有限维模型的矢量值过程的极值
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-08-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051826
Hui Xu, Mircea D. Grigoriu
Finite dimensional (FD) models, i.e., deterministic functions of time/space and finite sets of random variables, are constructed for target vector-valued random processes/fields. They are required to have two properties. First, standard Monte Carlo algorithms can be used to generate their samples, referred to as FD samples. Second, under some conditions specified by several theorems, FD samples can be used to estimate distributions of extremes and other functionals of target random functions. Numerical illustrations involving two-dimensional random processes and apparent properties of random microstructures are presented to illustrate the implementation of FD models for these stochastic problems and show that they are accurate if the conditions of our theorems are satisfied.
有限维(FD)模型,即时间/空间的确定性函数和随机变量的有限集,是为目标矢量值随机过程/场构建的。它们需要具备两个特性。首先,可使用标准蒙特卡罗算法生成样本,称为 FD 样本。其次,在一些定理规定的条件下,FD 样本可用于估计目标随机函数的极值分布和其他函数分布。我们给出了涉及二维随机过程和随机微结构明显特性的数值示例,以说明这些随机问题的 FD 模型的实现,并表明如果满足我们定理的条件,这些模型是准确的。
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引用次数: 0
Learning a class of stochastic differential equations via numerics-informed Bayesian denoising 通过数值信息贝叶斯去噪学习一类随机微分方程
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-07-01 DOI: 10.1615/int.j.uncertaintyquantification.2024052020
Zhanpeng Wang, Lijin Wang, Yanzhao Cao
Learning stochastic differential equations (SDEs) from observational data via neural networks is an important means of quantifying uncertainty in dynamical systems. The learning networks are typically built upon denoising the stochastic systems by harnessing their inherent deterministic nature, such as the Fokker-Planck equations related to SDEs. In this paper we propose the numerics-informed denoising by taking expectations on the Euler-Maruyama numerical scheme of SDEs, and then using the Bayesian neural networks (BNNs) to approximate the expectations through variational inference on the weights' posterior distribution. The approximation accuracy of the BNNs is analyzed. Meanwhiles we give a data acquisition method for learning non-autonomous differential equations (NADEs) which respects the time-variant nature of NADEs' flows. Numerical experiments on three models show effectiveness of the proposed methods.
通过神经网络从观测数据中学习随机微分方程(SDE)是量化动态系统不确定性的重要手段。学习网络通常是通过利用随机系统固有的确定性(如与 SDE 相关的 Fokker-Planck 方程)对其进行去噪而构建的。在本文中,我们提出了数值信息去噪方法,即对 SDE 的 Euler-Maruyama 数值方案进行期望,然后使用贝叶斯神经网络(BNN)通过对权重后验分布的变分推理来近似期望。分析了贝叶斯神经网络的近似精度。同时,我们给出了一种学习非自主微分方程(NADEs)的数据获取方法,该方法尊重 NADEs 流量的时变性。对三个模型的数值实验表明了所提方法的有效性。
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引用次数: 0
Covariance estimation using h-statistics in Monte Carlo and multilevel Monte Carlo methods 在蒙特卡洛和多级蒙特卡洛方法中使用 h 统计量进行协方差估计
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-07-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051528
Sharana Kumar Shivanand
We present novel Monte Carlo (MC) and multilevel Monte Carlo (MLMC) methods to determine the unbiased covariance of random variables using h-statistics. The advantage of this procedure lies in the unbiased construction of the estimator's mean square error in a closed form. This is in contrast to conventional MC and MLMC covariance estimators, which are based on biased mean square errors defined solely by upper bounds, particularly within the MLMC. The numerical results of the algorithms are demonstrated by estimating the covariance of the stochastic response of a simple 1D stochastic elliptic PDE such as Poisson's model.
我们提出了新颖的蒙特卡罗(MC)和多级蒙特卡罗(MLMC)方法,利用 h 统计法确定随机变量的无偏协方差。这种方法的优势在于能以封闭形式无偏构建估计器的均方误差。这与传统的 MC 和 MLMC 协方差估计器形成了鲜明对比,后者基于仅由上界定义的有偏均方误差,尤其是在 MLMC 内。通过估计泊松模型等简单一维随机椭圆 PDE 的随机响应协方差,演示了算法的数值结果。
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引用次数: 0
Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods 使用多级随机 Runge-Kutta 方法对部分观测扩散进行贝叶斯参数推断
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-06-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051131
Pierre Del Moral, Shulan Hu, Ajay Jasra, Hamza Ruzayqat, Xinyu Wang
We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to computeposterior expected values of the parametersfor a pre-specified mean square error (MSE); see cite{jasra_bpe_sde}. These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods forBayesian parameter estimation of static parameters for diffusions. Thiscan be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields.For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters.We provethat to achieve a MSE of $mathcal{O}(epsilon^2)$, for $epsilon>0$ given, the associated work is $mathcal{O}(epsilon^{-2})$.Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples.
我们考虑的问题是对部分离散观测的扩散过程相关静态参数进行贝叶斯估计。我们假设无法获得扩散过程的精确过渡动态,甚至无法获得无偏估计器,因此必须对扩散过程进行时间离散化。在这种情况下,已经证明了如何引入多级蒙特卡罗方法,以降低在预先指定的均方误差(MSE)下计算参数后置期望值的成本;见 cite{jasra_bpe_sde}。上述方法依赖于欧拉-马鲁山离散化方案,该方案在数值分析中具有众所周知的缓慢收敛特性。我们采用随机 Runge-Kutta (SRK) 方法对扩散的静态参数进行贝叶斯参数估计。对于一类扩散和 SRK 方法,我们考虑了参数的后验期望估计。我们证明,对于给定的 $epsilon>0$,要实现 $mathcal{O}(epsilon^{-2)$的 MSE,相关工作是 $mathcal{O}(epsilon^{-2})$。我们还通过几个数值例子来说明我们的方法。
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引用次数: 0
Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems 无穷维贝叶斯线性逆问题中信息增益的敏感性分析
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-05-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051416
Abhijit Chowdhary, Shanyin Tong, Georg Stadler, Alen Alexanderian
We study the sensitivity of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs) with respect to modeling uncertainties. In particular, we consider derivative-based sensitivity analysis of the information gain, as measured by the Kullback-Leibler divergence from the posterior to the prior distribution. To facilitate this, we develop a fast and accurate method for computing derivatives of the information gain with respect to auxiliary model parameters. Our approach combines low-rank approximations, adjoint-based eigenvalue sensitivity analysis, and post-optimal sensitivity analysis. The proposed approach also paves way for global sensitivity analysis by computing derivative-based global sensitivity measures. We illustrate different aspects of the proposed approach using an inverse problem governed by a scalar linear elliptic PDE, and an inverse problem governed by the three-dimensional equations of linear elasticity, which is motivated by the inversion of the fault-slip field after an earthquake.
我们研究了由偏微分方程(PDE)控制的无限维贝叶斯线性逆问题对建模不确定性的敏感性。特别是,我们考虑对信息增益进行基于导数的敏感性分析,信息增益是通过后验分布与先验分布之间的库尔贝克-莱布勒发散来衡量的。为了便于分析,我们开发了一种快速准确的方法,用于计算信息增益与辅助模型参数的导数关系。我们的方法结合了低阶近似、基于邻接的特征值灵敏度分析和后优化灵敏度分析。所提出的方法还通过计算基于导数的全局灵敏度度量,为全局灵敏度分析铺平了道路。我们用一个标量线性椭圆 PDE 所控制的逆问题和一个线性弹性三维方程所控制的逆问题来说明所提方法的不同方面。
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引用次数: 0
A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics 用于模型诊断的内嵌模型误差的贝叶斯校准框架
IF 1.7 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-05-01 DOI: 10.1615/int.j.uncertaintyquantification.2024051602
Arun Hegde, Elan Weiss, Wolfgang Windl, Habib N. Najm, Cosmin Safta
We study the utility and performance of a Bayesian model error embedding construction in the context of molecular dynamics modeling of metallic alloys, where we embed model error terms in existing interatomic potential model parameters. To alleviate the computational burden of this approach, we propose a framework combining likelihood approximation and Gaussian process surrogates. We leverage sparse Gaussian process techniques to construct a hierarchy of increasingly accurate but more expensive surrogate models. This hierarchy is then exploited by multilevel Markov chain Monte Carlo methods to efficiently sample from the target posterior distribution. We illustrate the utility of this approach by calibrating an interatomic potential model for a family of gold-copper alloys. In particular, this case study highlights effective means for dealing with computational challenges with Bayesian model error embedding in large-scale physical models, and the utility of embedded model error for model diagnostics.
我们研究了贝叶斯模型误差嵌入结构在金属合金分子动力学建模中的实用性和性能,我们将模型误差项嵌入现有的原子间势模型参数中。为了减轻这种方法的计算负担,我们提出了一个结合似然逼近和高斯过程代理的框架。我们利用稀疏高斯过程技术,构建了一个精确度越来越高但成本越来越高的代用模型层次结构。然后,多级马尔科夫链蒙特卡罗方法利用这一层次结构,从目标后验分布中高效采样。我们通过校准金铜合金系列的原子间势垒模型来说明这种方法的实用性。本案例研究特别强调了应对大规模物理模型中贝叶斯模型误差嵌入计算挑战的有效方法,以及嵌入模型误差对模型诊断的实用性。
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引用次数: 0
期刊
International Journal for Uncertainty Quantification
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