Parametric dynamic mode decomposition for reduced order modeling

Higher Order Dynamic Mode Decomposition

On Reduced Input-Output Dynamic Mode Decomposition

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches f...

Numerical Simulation and Parametric Reduced Order Modeling of the Natural Convection of Water-Copper Nanofluid

In this article, a coupled computational framework is presented for the numerical simulation of mass transfer under the effects of natural convection phenomena in a field contains water-copper Nano-fluid. This CFD model is build up based on accurate algorithms for spatial derivatives and time integration. The spatial derivatives have been calculated using first order upwind and second order cen...

Improved Reduced Order Modeling Strategies for Coupled and Parametric Systems

Parametric Reduced-Order Models

We address the problem of propagating input uncertainties through a computational fluid dynamics model. Methods such as Monte Carlo simulation can require many thousands (or more) of computational fluid dynamics solves, rendering them prohibitively expensive for practical applications. This expense can be overcome with reduced-order models that preserve the essential flow dynamics. The specific...