CD-ROM: Complemented Deep-Reduced Order Model

Understand

Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems.

• However, the applicability of the method to non linear high-dimensional dynamical systems such as the Navier-Stokes equations has been shown to be limited, producing inaccurate and sometimes unstable models.
• This paper proposes a deep learning based closure modeling approach for classical POD-Galerkin reduced order models (ROM).
• The proposed approach is theoretically grounded, using neural networks to approximate well studied operators.