Extending the explicit constraint force method to inverse problems

TL;DR

This paper extends the explicit constraint force method (ECFM) to inverse problems, demonstrating its effectiveness in parameter estimation, dynamic problems, noisy data, stochastic models, and recovering boundary conditions, offering a new alternative for inverse analysis.

Contribution

The paper introduces novel extensions of ECFM to inverse problems, including dynamic, noisy, stochastic, and boundary recovery scenarios, broadening its applicability.

Findings

ECFM effectively estimates parameters from sparse data.

Extension to dynamic and noisy problems shows robustness.

Stochastic ECFM incorporates polynomial chaos for uncertainty quantification.

Abstract

Recently, the explicit constraint force method (ECFM) was introduced as a principled approach to solution reconstruction in the presence of missing physics. In solution reconstruction, parameters of a physical model are estimated from sparse measurement data as a means to obtain the full solution field. In contrast, inverse problems target the missing parameters and estimate the solution along the way. Noting the similarity of the mathematical formulations of these two tasks, we investigate the use of ECFM to solve inverse problems. First, we compare the ECFM formulation of the inverse problem to a standard approach using two numerical examples. The first example provides an extension of ECFM to dynamic problems, and the second offers a novel approach to treat noisy measurement data. Next, we introduce a method to solve inverse problems for which the parameterized model has stochastic components. This approach is based on constraint forces and the polynomial chaos expansion, and is illustrated with another numerical example. Finally, we discuss extensions of ECFM to recover missing boundary conditions and domain geometries from measurement data, which are shown to be special cases of problems treated previously in the literature. The purpose of this work is to extend the mathematical framework of ECFM to novel applications and to gauge the method's viability as an alternative strategy for inverse analysis.