THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

A data-driven framework is proposed towards the end of predictive modeling of complex
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …

In this work we present a Reduced Order Model which is specifically designed to deal with
turbulent flows in a finite volume setting. The method used to build the reduced order model …

This paper puts forth two new closure models for the proper orthogonal decomposition
reduced-order modeling of structurally dominated turbulent flows: the dynamic subgrid-scale …

This paper proposes a supervised machine learning framework for the non-intrusive model
order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state …

We investigate a hierarchy of eddy-viscosity terms in proper orthogonal decomposition
(POD) Galerkin models to account for a large fraction of unresolved fluctuation energy …

Numerical simulation of fluid flows requires important computational efforts but it is essential
in engineering applications. Reduced Order Model (ROM) can be employed whenever fast …

Many reduced-order models are neither robust with respect to parameter changes nor cost-
effective enough for handling the nonlinear dependence of complex dynamical systems. In …

We generalize the POD-based Galerkin method for post-transient flow data by incorporating
Navier–Stokes equation constraints. In this method, the derived Galerkin expansion …

Proper Orthogonal Decomposition Methods for Partial Differential Equations evaluates the
potential applications of POD reduced-order numerical methods in increasing computational …