Learning stable reduced-order models for hybrid twins

Abstract The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability powerful machine learning techniques. This twin combines physics-based models within model order reduction framework—to obtain real-time feedback rates—and data science. Thus, main idea HT is develop on-the-fly data-driven correct possible deviations between measurements and predictions. paper focused on computation stable, fast, accurate corrections in framework. Furthermore, regarding delicate important problem stability, new approach proposed, introducing several subvariants guaranteeing low computational cost as well achievement stable time-integration.