Assessment of unsteady flow predictions using hybrid deep learning based reduced order models

Understand

In this paper, we present two deep learning-based hybrid data-driven reduced order models for the prediction of unsteady fluid flows.

• The first model projects the high-fidelity time series data from a finite element Navier-Stokes solver to a low-dimensional subspace via proper orthogonal decomposition (POD).
• The time-dependent coefficients in the POD subspace are propagated by the recurrent net (closed-loop encoder-decoder updates) and mapped to a high-dimensional state via the mean flow field and POD basis vectors.
• This model is referred as POD-RNN.