Generating a digital twin of any complex system requires modeling and
computational approaches that are efficient, accurate, and modular. Traditional
reduced order modeling techniques are targeted at only the first two but the
novel non-intrusive approach presented in this study is an attempt at taking
all three into account effectively compared to their traditional counterparts.
Based on dimensionality reduction using proper orthogonal decomposition (POD),
we introduce a long short-term memory (LSTM) neural network architecture
together with a principal interval decomposition (PID) framework as an enabler
to account for localized modal deformation, which is a key element in accurate
reduced order modeling of convective flows. Our applications for convection
dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations
demonstrate that the proposed approach yields significantly more accurate
predictions than the POD-Galerkin method, and could be a key enabler towards
near real-time predictions of unsteady flows
