9 citations · 20 across the 14 of their papers we have counts for
4 papers · 1 filter
Spatio-temporal Latent Denoising Diffusion Probabilistic Models for Reduced-order Modeling of Parametrized Dynamical Systems
Michiel Nikken, Nicolò Botteghi, Federico Califano +1
Many scientific problems require accurate modeling of complex physical phenomena, such as fluid dynamics or climate modeling. These phenomena often result in high-dimensional and t…
Model reduction of port-Hamiltonian systems via neural networks
Silke Glas, Alexander Heinlein, Harald Monsuur +1
In this paper, we consider structure-preserving model reduction of port-Hamiltonian (pH) systems which extend classical Hamiltonian systems with dissipation and an input-output por…
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Nicolò Botteghi, Silke Glas, Christoph Brune
Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of parameter-dependent high-dimensional dynamical systems is crucial in many applications i…
Structure-Preserving Generalized Manifold Galerkin Reduction for Port-Hamiltonian Systems
Silke Glas, Hongliang Mu
This paper considers structure-preserving model order reduction (MOR) techniques for port-Hamiltonian (pH) systems, which are typically derived from energy-based modeling. To keep…