ABSTRACT The rapid advancement of industrial technologies, data collection, and handling methods has paved the way for the widespread adoption of digital twins (DTs) in engineering, enabling seamless integration between physical systems and their virtual counterparts. The current work presents a comprehensive framework for building robust and scalable DTs tailored for material testing applications using a universal testing machine (UTM), focusing on core challenges such as model fidelity, data integration, and computational efficiency. Our goal is to build a DT of a material subjected to cyclic loading. A linear elastic material model with its governing partial differential equation (PDE) and discretization through the finite element method (FEM) is considered. The obtained high‐fidelity solution is used within the framework of the reduced basis method (RBM) to construct a reduced model with the help of a well‐known greedy method, which significantly improves the computational efficiency. The solution is further improved by integrating data collected by sensors in real‐time through the parametrized‐background‐data‐weak (PBDW) method. The presented approach integrates physical knowledge, which is available in terms of constitutive or material modeling, and real‐time sensor data to construct and continuously update the DTs. Emphasis is placed on the knowledge available through PDEs, which address the reliability and robustness of the DT model. This work highlights the advantages of the DTs in predictive maintenance and health monitoring of assets or systems, which eliminates unexpected failures and downtimes in engineering applications.
Aylwin et al. (Sun,) studied this question.