A novel, finite-element-based framework for sparse data solution reconstruction and multiple choices

TL;DR

This paper introduces a novel finite-element-based framework for real-time inverse analysis that reconstructs full field solutions from sparse data without traditional inverse methods or machine learning, enabling enhanced digital twinning and system control.

Contribution

It presents a new FE-based inverse analysis method capable of solving problems with unknown boundary conditions without traditional inverse techniques or machine learning.

Findings

Feasible to reconstruct solutions from sparse data in real time.

Introduces a controlled multiple solution generation approach.

Demonstrates potential for semi-autonomous system control.

Abstract

Digital twinning offers a capability of effective real-time monitoring and control, which are vital for cost-intensive experimental facilities, particularly the ones where extreme conditions exist. Sparse experimental measurements collected by various diagnostic sensors are usually the only source of information available during the course of a physical experiment. Consequently, in order to enable monitoring and control of the experiment (digital twinning), the ability to perform inverse analysis, facilitating the full field solution reconstruction from the sparse experimental data in real time, is crucial. This paper shows for the first time that it is possible to directly solve inverse problems, such as solution reconstruction, where some or all boundary conditions (BCs) are unknown, by purely using a finite-element (FE) approach, without needing to employ any traditional inverse analysis techniques or any machine learning models, as is normally done in the field. This novel and efficient FE-based inverse analysis framework employs a conventional FE discretisation, splits the loading vector into two parts corresponding to the known and unknown BCs, and then defines a loss function based on that split. In spite of the loading vector split, the loss function preserves the element connectivity. This function is minimised using a gradient-based optimisation. Furthermore, this paper presents a novel modification of this approach, which allows it to generate a range of different solutions satisfying given requirements in a controlled manner. Controlled multiple solution generation in the context of inverse problems and their intrinsic ill-posedness is a novel notion, which has not been explored before. This is done in order to potentially introduce the capability of semi-autonomous system control with intermittent human intervention to the workflow.