Data-driven probability concentration and sampling on manifold

JOURNAL OF COMPUTATIONAL PHYSICS

卷:321

Article

Soize, C.¹  Ghanem, R.² 

[1] Univ Paris Est, CNRS, Lab Modelisat & Simulat Multiechelle, MSME UMR 8208, 5 Bd Descartes, F-77454 Marne La Vallee 2, France

[2] Univ So Calif, 210 KAP Hall, Los Angeles, CA 90089 USA

关键词: Concentration of probability;    Measure concentration;    Probability distribution on manifolds;    Random sampling generator;    MCMC generator;    Diffusion maps;    Statistics on manifolds;    Design of experiments for random parameters;   

DOI  :  10.1016/j.jcp.2016.05.044

来源: Elsevier

【 摘 要 】

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while apriori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free