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