Dynamic Data-driven Bayesian GMsFEM

TL;DR

This paper introduces a Bayesian multiscale finite element method that probabilistically selects important degrees of freedom using dynamic observational data to improve solutions of parabolic equations in heterogeneous media.

Contribution

It develops a Bayesian framework for GMsFEM that incorporates dynamic data to probabilistically identify unresolved scales and enhance multiscale modeling accuracy.

Findings

Probabilistic selection of basis functions improves solution accuracy.

The method effectively models uncertainties in subgrid information.

Dynamic data integration enhances multiscale model reliability.

Abstract

In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabilistically in a Generalized multiscale finite element method framework. Due to scale disparity in many multiscale applications, computational models can not resolve all scales. Dominant modes in the Generalized Multiscale Finite Element Method are used as "permanent" basis functions, which we use to compute an inexpensive multiscale solution and the associated uncertainties. Through our Bayesian framework, we can model approximate solutions by selecting the unresolved scales probabilistically. We consider parabolic equations in heterogeneous media. The temporal domain is partitioned into subintervals. Using residual information and given dynamic data, we design appropriate prior distribution for modeling missing subgrid information. The likelihood is designed to minimize the residual in the underlying PDE problem and the mismatch of observational data. Using the resultant posterior distribution, the sampling process identifies important degrees of freedom beyond permanent basis functions. The method adds important degrees of freedom in resolving subgrid information and ensuring the accuracy of the observations.