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Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed.
Machine learning based digital twin for dynamical systems with multiple time-scales
Our approach strategically separates into two components -- (a) a physics-based nominal model for data processing and response predictions, and (b) a data-driven machine learning model for the time-evolution of the system parameters.
The role of surrogate models in the development of digital twins of dynamic systems
As digital twins are also expected to exploit data and computational methods, there is a compelling case for the use of surrogate models in this context.
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