Virtual Measurement

Virtual measurement leverages computational models to infer properties or characteristics of a system without direct physical measurement, aiming to improve efficiency, reduce costs, and enable 100% inspection rates. Current research focuses on developing probabilistic machine learning models, including generative models and Bayesian methods, to estimate uncertainty and integrate virtual measurements with existing quality management frameworks. These techniques find applications in diverse fields, from fluid dynamics simulations and industrial quality control to healthcare attribute prediction and autonomous driving map generation, offering significant improvements in data analysis and decision-making.