Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning. (arXiv:2206.08659v1 [math.NA])

Web: http://arxiv.org/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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