One approach to parametric and adaptive model reduction is via the interpolation of
orthogonal bases, subspaces or positive definite system matrices. In all these cases, the …

We develop an unsupervised machine learning algorithm for the automated discovery and
identification of traveling waves in spatiotemporal systems governed by partial differential …

The proper orthogonal decomposition (POD) is the prevailing method for basis generation in
the model reduction of fluids. A serious limitation of this method, however, is that it is …

A novel approach to reduced-order modeling of high-dimensional systems with time-varying
properties is proposed. It combines the problem formulation of the Dynamic Mode …

One approach to parametric and adaptive model reduction is via the interpolation of
orthogonal bases, subspaces or positive definite system matrices. In all these cases, the …

The necessity to include dynamic mean field representations in low order Galerkin models,
and the role and form of such representations, are explored along natural and forced …

We review a strategy for low-and least-order Galerkin models suitable for the design of
closed-loop stabilization of wakes. These low-order models are based on a fixed set of …

Ь жа зи ж зйаиз г гл зи а ин з гл и б в и г и жзи згайи гвз д ж аа а Дгж в жан д ж аа аЕ гл
ззйбди гв в в гв и л и и ж а гл в ийж К агл зи а ин в анз з л з гб в и н д ж аа а гл ззйбди гв а …

A Galerkin method is presented for control-oriented reduced-order models (ROM). This
method generalizes linear approaches elaborated by M. Morzyński et al. for the nonlinear …

Reduced-Order Modeling of Complex Engineering and Geophysical Flows: Analysis and
Computations Page 1 Reduced-Order Modeling of Complex Engineering and Geophysical …