Bridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learning

Abstract

AbstractDigital twins (DTs) enable powerful predictive analytics, but persistent discrepancies between simulations and real systems—known as the reality gap—undermine their reliability. Coined in robotics, the term now applies to DTs, where discrepancies ...Practical ApplicationsDigital twins (DTs) are powerful virtual replicas of physical assets like bridges used to monitor health and predict maintenance needs. However, their reliability is often undermined by a “reality gap”—a mismatch between the ...