• Thiago Ritto, Universidade Federal Do Rio De Janeiro
• Anas Batou, Université Gustave Eiffel
• David Barton, University of Bristol
• David Wagg, University of Sheffield

A digital twin is an ingenious concept that helps in organizing different areas of expertise aiming at supporting engineering and management decisions related to a specific asset; it comprises computational models, sensors, learning, real-time analysis, diagnosis, prognosis, and so on. In this context, the performance assessment of dynamical structures may be subject to multiple sources of uncertainties related to the parameters of the system, its environment, the experimental setting, etc. The objective of this mini-symposium is to present new advances broadly related to digital twins for linear and nonlinear dynamic structures in the presence of uncertainties. Topics relevant to this minisymposium include, but are not limited to:
- Methods for modelling and propagating uncertainties in structural dynamics.
- Combination of physics-based models and machine learning tools in structural dynamics.
- Experimental parameter and system identification/updating.
- Design of experiments (sensor placement,etc) in presence of uncertainties.
- Robust design methods related to structural dynamics.
- Reliability analysis of dynamical structures.