Model selection for inverse problems: Best choice of basis function and model order selection

TL;DR

This paper presents a Bayesian framework for selecting the optimal basis functions and model order in inverse problems, emphasizing their importance in low-data scenarios, demonstrated through an elastic electron scattering application.

Contribution

It introduces a comprehensive Bayesian method for basis and model order selection in inverse problems, addressing a gap in existing approaches.

Findings

Effective basis and model order selection improves inverse problem solutions.

Application to elastic electron scattering demonstrates practical utility.

Method enhances accuracy in low-data inverse problems.

Abstract

A complete solution for an inverse problem needs five main steps: choice of basis functions for discretization, determination of the order of the model, estimation of the hyperparameters, estimation of the solution, and finally, caracterisation of the proposed solution. Many works have been done for the three last steps. The two first have been neglected for a while, in part due to the complexity of the problem. However, in many inverse problems, particularly when the number of data is very low, a good choice of the basis functions and a good selection of the order become primordial. In this paper, we first propose a complete solution whithin a Bayesian framework. Then, we apply the proposed method to an inverse elastic electron scattering problem.