In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-
order) aerodynamic models or flight testing are some of the fundamental but complex steps …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …

This paper presents a review of proper orthogonal decomposition (POD) methods for order
reduction in a variety of research areas. The historical development and basic mathematical …

Abstract Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …

An adaptive projection-based reduced-order model (ROM) formulation is presented for
model-order reduction of problems featuring chaotic and convection-dominant physics. An …

Higher Order Dynamic Mode Decomposition and Its Applications provides detailed
background theory, as well as several fully explained applications from a range of industrial …

The large‐scale structure systems in engineering are complex, high dimensional, and
variety of physical mechanism couplings; it will be difficult to analyze the dynamic behaviors …

This paper presents data-driven learning of localized reduced models. Instead of a global
reduced basis, the approach employs multiple local approximation subspaces. This …