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

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …

Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …

SUMMARY A rigorous computational framework for the dimensional reduction of discrete,
high‐fidelity, nonlinear, finite element structural dynamics models is presented. It is based …

Large linear systems of saddle point type arise in a wide variety of applications throughout
computational science and engineering. Due to their indefiniteness and often poor spectral …

In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and
wide applicability for simulating large-scale mathematical models in engineering and the …

This paper derives predictive reduced-order models for rocket engine combustion dynamics
via Operator Inference, a scientific machine learning approach that blends data-driven …

In this paper, popular model reduction techniques from the fields of structural dynamics,
numerical mathematics and systems and control are reviewed and compared. The …