Paper Info
Title
MULTILEVEL HIERARCHICAL DECOMPOSITION OF FINITE ELEMENT WHITE NOISE WITH APPLICATION TO MULTILEVEL MARKOV CHAIN MONTE CARLO
Abstract
In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable to multilevel MCMC frameworks which build finescale random fields in a hierarchical fashion from coarse-scale random fields. Our new hierarchical multilevel method relies on a hierarchical decomposition of the white noise source function in L-2 which allows us to form Gaussian random field realizations across multiple levels of discretization in a way that fits into multilevel MCMC algorithmic frameworks. After presenting our main theoretical results and numerical scaling results to showcase the utility of this new hierarchical PDE method for generating Gaussian random field realizations, this method is tested on a four-level MCMC algorithm to explore its feasibility.
Year
DOI
Venue
2021
10.1137/20M1349606
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
DocType
Volume
Gaussian random field, nonlinear Bayesian inference, Markov chain Monte Carlo, multilevel Markov chain Monte Carlo, high-dimensional uncertainty quantification, algebraic multigrid
Journal
43
Issue
ISSN
Citations 
5
1064-8275
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Hillary R. Fairbanks100.34
Umberto Villa2306.64
Panayot S. Vassilevski370.97