Adaptive anisotropic Bayesian meshing for inverse problems

TL;DR

This paper introduces an adaptive anisotropic Bayesian meshing approach for inverse problems, iteratively refining discretization to improve accuracy while reducing computational costs.

Contribution

It proposes a novel method that treats discretization as an unknown to be optimized, allowing anisotropic mesh refinement in inverse problems.

Findings

Significant reduction in memory usage and computation time.

Improved accuracy in parameter estimation.

Efficient adaptive mesh refinement strategy.

Abstract

We consider inverse problems estimating distributed parameters from indirect noisy observations through discretization of continuum models described by partial differential or integral equations. It is well understood that the errors arising from the discretization can be detrimental for ill-posed inverse problems, as discretization error behaves as correlated noise. While this problem can be avoided with a discretization fine enough to suppress the modeling error level below that of the exogenous noise that is addressed, e.g., by regularization, the computational resources needed to deal with the additional degrees of freedom may require high performance computing environment. Following an earlier idea, we advocate the notion that the discretization is one of the unknowns of the inverse problem, and is updated iteratively together with the solution. In this approach, the discretization, defined in terms of an underlying metric, is refined selectively only where the representation power of the current mesh is insufficient. In this paper we allow the metrics and meshes to be anisotropic, and we show that this leads to significant reduction of memory allocation and computing time.