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 …

Nearly all model-reduction techniques project the governing equations onto a linear
subspace of the original state space. Such subspaces are typically computed using methods …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

Classical model reduction techniques project the governing equations onto linear
subspaces of the high-dimensional state-space. For problems with slowly decaying …

We introduce a data‐driven model order reduction approach that represents an extension of
the Loewner framework for linear and bilinear systems to the case of quadratic‐bilinear (QB) …

Machine learning and artificial neural networks have shown to be applicable in modeling
and simulation of complex physical phenomena as well as creating surrogate models …

With the ubiquitous use of computers in controlling physical systems, it requires to have a
new formalism that could model both continuous flows and discrete jumps. Hybrid systems …

This work proposes a model-reduction approach for the material point method on nonlinear
manifolds. Our technique approximates the kinematics by approximating the deformation …

Classical model reduction techniques project the governing equations onto linear
subspaces of the high-dimensional state-space. For problems with slowly decaying …

The model order reduction (MOR) enterprise has shown unprecedented applications in the
area of power systems by allowing tractable and realistic simulations of complex power …