jInv -- a flexible Julia package for PDE parameter estimation

TL;DR

jInv is an open-source Julia package that offers parallel algorithms for efficient PDE parameter estimation from noisy measurements, addressing computational challenges in inverse problems across various scientific fields.

Contribution

The paper introduces jInv, a flexible, parallel Julia framework for PDE parameter estimation, enabling scalable solutions for large measurement datasets.

Findings

Demonstrates effective parallelization schemes for inverse problems.

Shows applicability to multiphysics geophysical imaging.

Provides a user-friendly, extensible software package.

Abstract

Estimating parameters of Partial Differential Equations (PDEs) from noisy and indirect measurements often requires solving ill-posed inverse problems. These so called parameter estimation or inverse medium problems arise in a variety of applications such as geophysical, medical imaging, and nondestructive testing. Their solution is computationally intense since the underlying PDEs need to be solved numerous times until the reconstruction of the parameters is sufficiently accurate. Typically, the computational demand grows significantly when more measurements are available, which poses severe challenges to inversion algorithms as measurement devices become more powerful. In this paper we present jInv, a flexible framework and open source software that provides parallel algorithms for solving parameter estimation problems with many measurements. Being written in the expressive programming language Julia, jInv is portable, easy to understand and extend, cross-platform tested, and well-documented. It provides novel parallelization schemes that exploit the inherent structure of many parameter estimation problems and can be used to solve multiphysics inversion problems as is demonstrated using numerical experiments motivated by geophysical imaging.