# Bayesian Multiscale Finite Element Methods. Modeling missing subgrid information probabilistically

## TL;DR

This paper introduces a Bayesian multiscale finite element method that models un-resolved subgrid scales probabilistically, providing multiple solutions and a rigorous uncertainty quantification framework for complex PDEs in heterogeneous media.

## Contribution

It develops a Bayesian framework for multiscale finite element methods, incorporating residual-based priors and posteriors, and introduces efficient sampling strategies for probabilistic subgrid modeling.

## Key findings

- Multiple inexpensive solutions for deterministic problems
- Effective residual-based sampling algorithms demonstrated
- Probabilistic description improves subgrid scale modeling

## Abstract

In this paper, we develop a Bayesian multiscale approach based on a multiscale finite element method. Because of scale disparity in many multiscale applications, computational models can not resolve all scales. Various subgrid models are proposed to represent un-resolved scales. Here, we consider a probabilistic approach for modeling un-resolved scales using the Multiscale Finite Element Method (cf., [1, 2]). By representing dominant modes using the Generalized Multiscale Finite Element, we propose a Bayesian framework, which provides multiple inexpensive (computable) solutions for a deterministic problem. These approximate probabilistic solutions may not be very close to the exact solutions and, thus, many realizations are needed. In this way, we obtain a rigorous probabilistic description of approximate solutions. In the paper, we consider parabolic and wave equations in heterogeneous media. In each time interval, the domain is divided into subregions. Using residual information, we design appropriate prior and posterior distributions. The likelihood consists of the residual minimization. To sample from the resulting posterior distribution, we consider several sampling strategies. The sampling involves identifying important regions and important degrees of freedom beyond permanent basis functions, which are used in residual computation. Numerical results are presented. We consider two sampling algorithms. The first algorithm uses sequential sampling and is inexpensive. In the second algorithm, we perform full sampling using the Gibbs sampling algorithm, which is more accurate compared to the sequential sampling. The main novel ingredients of our approach consist of: defining appropriate permanent basis functions and the corresponding residual; setting up a proper posterior distribution; and sampling the posteriors.