# Variational Bayes' method for functions with applications to some inverse problems

## TL;DR

This paper extends the Variational Bayes' method to infinite-dimensional spaces for solving inverse PDE problems, providing a computationally efficient alternative to traditional sampling methods with proven effectiveness.

## Contribution

It generalizes finite-dimensional VBM to infinite-dimensional spaces and establishes a rigorous theoretical framework for inverse PDE problems.

## Key findings

- Effective approximation of posterior measures in inverse PDE problems
- Theoretical validation of infinite-dimensional VBM
- Numerical examples demonstrate method's efficiency

## Abstract

Bayesian approach, as a useful tool for quantifying uncertainties, has been widely used for solving inverse problems of partial differential equations (PDEs). One of the key difficulties for employing Bayesian approach for the issue is how to extract information from the posterior probability measure. Variational Bayes' method (VBM) is firstly and broadly studied in the field of machine learning, which has the ability to extract posterior information approximately by using much lower computational resources compared with the conventional sampling type methods. In this paper, we generalize the usual finite-dimensional VBM to infinite-dimensional space, which makes the usage of VBM for inverse problems of PDEs rigorously. We further establish general infinite-dimensional mean-field approximate theory, and apply this theory to abstract linear inverse problems with Gaussian and Laplace noise assumptions. The results on some numerical examples substantiate the effectiveness of the proposed approach.