ABSTRACT We adopt the following definition of a Digital Twin [1]: A digital twin is defined as a virtual representation of a physical asset, or a process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and decision-making. To enable predictive twins, we utilize Hybrid Analysis and Modelling (HAM) [2] that combines classical Physic-Based Methods (PBM) accelerated by means of Reduced Order Modelling (ROM) [3] together with Data-Driven Methods (DDM) based on sensor measurement analysed by use of Machine Learning (ML). We will in this talk present different ROM and HAM techniques applicable for solid, structural, and fluid mechanics. REFERENCES [1] A. Rasheed, O. San, T. Kvamsdal. Digital Twin: Values, Challenges and Enablers from a Modeling Perspective. IEEE Access, 8:21980--22012, 2020, 2020. [2] O. San, A. Rasheed, T. Kvamsdal. Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution. GAMM‐Mitteilungen, e202100007. 2021. [3] E. Fonn, H. v. Brummelen, J. L. Eftang, T. Rusten, K. A. Johannessen, T. Kvamsdal, A. Rasheed, Least-Squares Projected Models for Non-Intrusive Affinization of Reduced Basis Methods. International Journal for Numerical Methods in Engineering 126, no. 18 (2025): e70127, https://doi.org/10.1002/nme.70127