Automated LabVIEW-Based Digital Twin Calibration and Validation for Multi-Physics System Simulation

**Abstract:** This paper details a novel system for autonomously calibrating and validating digital twins of complex multi-physics systems, leveraging LabVIEW's graphical programming environment and advanced statistical machine learning techniques. The system addresses the critical challenge of ensuring digital twin fidelity by integrating real-time data from physical systems with simulation models, dynamically adjusting model parameters to minimize discrepancies. Through automated protocol generation, data assimilation, uncertainty quantification, and validation scoring, this approach significantly reduces the time and expertise required to deploy accurate and reliable digital twins, enabling enhanced predictive capabilities and optimized system performance. The overarching aim is to provide a scalable, self-optimizing digital twin validation pipeline tailored specifically for LabVIEW implementations, addressing a burgeoning market need across industries requiring high-fidelity virtual representation of physical assets.