Data-driven probability concentration and sampling on manifold

TL;DR

This paper introduces a novel data-driven methodology combining kernel density estimation, MCMC sampling, diffusion maps, and reduced-order modeling to generate statistically consistent realizations of random vectors concentrated on unknown manifolds, with proven convergence and robustness.

Contribution

The paper presents a new integrated approach for probabilistic modeling on unknown manifolds using advanced data analysis and sampling techniques, enhancing uncertainty quantification and stochastic modeling.

Findings

Method is robust to noise and data complexity.

Effective in capturing data geometry and structure.

Validated through three complex numerical applications.

Abstract

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 data set of observations of this vector. The probability distribution of this random vector, while a-priori 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 data set. 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 data set, 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 data set. 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 data sets. 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.