Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning

@article{Bistrian2022DigitalTD,
  title={Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning},
  author={Diana Alina Bistrian and Omer San and Ionel M. Navon},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.08659}
}
A digital twin is a surrogate model that has the main feature to mirror the original process behavior. Associating the dynamical process with a digital twin model of reduced complexity has the significant advantage to map the dynamics with high accuracy and reduced costs in CPU time and hardware to timescales over which that suffers significantly changes and so it is difficult to explore. This paper introduces a new framework for creating efficient digital twin models of fluid flows. We… 
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