Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
A survey of projection-based model reduction methods for parametric dynamical systems
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 …
a wide range of complex physical phenomena; however, the inherent large-scale nature of …
A Grassmann manifold handbook: Basic geometry and computational aspects
The Grassmann manifold of linear subspaces is important for the mathematical modelling of
a multitude of applications, ranging from problems in machine learning, computer vision and …
a multitude of applications, ranging from problems in machine learning, computer vision and …
[图书][B] Model order reduction for differential-algebraic equations: a survey
In this paper, we discuss the model order reduction problem for descriptor systems, that is,
systems with dynamics described by differential-algebraic equations. We focus on linear …
systems with dynamics described by differential-algebraic equations. We focus on linear …
Geometric subspace updates with applications to online adaptive nonlinear model reduction
In many scientific applications, including model reduction and image processing, subspaces
are used as ansatz spaces for the low-dimensional approximation and reconstruction of the …
are used as ansatz spaces for the low-dimensional approximation and reconstruction of the …
[PDF][PDF] Manifold interpolation
R Zimmermann - Model Order Reduction, 2021 - library.oapen.org
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 …
orthogonal bases, subspaces or positive definite system matrices. In all these cases, the …
Real time simulation for computational surgery: a review
E Cueto, F Chinesta - Advanced Modeling and Simulation in Engineering …, 2014 - Springer
In this paper a non-exhaustive review is made on the existing literature for real-time
simulation in the field of computational surgery. Many methods have been proposed so far to …
simulation in the field of computational surgery. Many methods have been proposed so far to …
[HTML][HTML] Model reduction on manifolds: a differential geometric framework
Using nonlinear projections and preserving structure in model order reduction (MOR) are
currently active research fields. In this paper, we provide a novel differential geometric …
currently active research fields. In this paper, we provide a novel differential geometric …
Gaussian process subspace prediction for model reduction
Subspace-valued functions arise in a wide range of problems, including parametric reduced
order modeling (PROM), parameter reduction, and subspace tracking. In PROM, each …
order modeling (PROM), parameter reduction, and subspace tracking. In PROM, each …
Manifold interpolation and model reduction
R Zimmermann - arXiv preprint arXiv:1902.06502, 2019 - arxiv.org
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 …
orthogonal bases, subspaces or positive definite system matrices. In all these cases, the …