In today's technological world, physical and artificial processes are mainly described by
mathematical models, which can be used for simulation or control. These processes are …

Reduced order models are useful for accelerating simulations in many‐query contexts, such
as optimization, uncertainty quantification, and sensitivity analysis. However, offline training …

Computing the singular values and vectors of a matrix is a crucial kernel in numerous
scientific and industrial applications. As such, numerous methods have been proposed to …

Data is continuously being generated from sources such as machines, network traffic,
application logs, etc. Timely and accurate detection of anomalies in massive data streams …

Projection‐based nonlinear model order reduction (MOR) methods typically make use of a
reduced basis to approximate high‐dimensional quantities. However, the most popular …

This work introduces an online greedy method for constructing quadratic manifolds from
streaming data, designed to enable in-situ analysis of numerical simulation data on the …

Our approach aims at coupling the ever increasing off-line computing power of mainframe
computers with the interactive on-line possibilities of ubiquitous low computing power …

In our earlier work Fareed et al.(2018), we proposed an incremental SVD algorithm with
respect to a weighted inner product to compute the proper orthogonal decomposition (POD) …

The construction of a nonlinear reduced-order model for fluid–structure interaction problems
is investigated in this paper for unsteady compressible flows excited by the rigid body motion …

In our earlier work Fareed et al.(2018)[18], we developed an incremental approach to
compute the proper orthogonal decomposition (POD) of PDE simulation data. Specifically …