Physics Informed Neural Networks for Electrical Impedance Tomography

Abstract

Electrical Impedance Tomography (EIT) is an imaging modality used to reconstruct the internal conductivity distribution of a domain based on boundary voltage measurements. In this paper, we present a novel EIT approach for integrated sensing of composite materials and structures utilizing Physics Informed Neural Networks (PINNs). Unlike traditional data-driven only models, PINNs incorporate underlying physical principles governing EIT directly into the learning process, enabling precise and rapid reconstructions. We demonstrate the effectiveness of PINNs with a variety of physical constraints for integrated sensing. The proposed approach has the potential to enhance material characterization and condition monitoring, offering a robust alternative to classical EIT approaches.