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

The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …

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 …

Model reduction is ubiquitous in computational science and engineering. It plays a key role
in making computationally tractable outer-loop applications that require simulating systems …

Deep neural networks have been shown to provide accurate function approximations in high
dimensions. However, fitting network parameters requires informative training data that are …

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

Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …

Reduced-order models are indispensable for multi-query or real-time problems. However,
there are still many challenges to constructing efficient ROMs for time-dependent …

This work proposes an adaptive structure-preserving model order reduction method for finite-
dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena. To …

We propose a nonlinear registration-based model reduction procedure for rapid and reliable
solution of parameterized two-dimensional steady conservation laws. This class of problems …