4 papers
Hyper-reduction-free reduced-order Newton solvers for projection-based model-order reduction of nonlinear dynamical systems
Liam K. Magargal, Parisa Khodabakhshi, Steven N. Rodriguez
This study proposes an intrusive projection-based model-order reduction framework for nonlinear problems with a polynomial structure, solved iteratively using a Newton solver in th…
Efficient implementation of graph autoencoders for model-order reduction of systems with sharp gradients
Liam K Magargal, Parisa Khodabakhshi
This study investigates the efficient deployment of graph autoencoders, a class of graph neural networks (GNNs), for model-order reduction (MOR) of high-dimensional dynamical syste…
Meshless projection model-order reduction via reference spaces for smoothed-particle hydrodynamics
Steven N. Rodriguez, Steven L. Brunton, Liam K. Magargal +6
A model-order reduction framework for the meshless smoothed-particle hydrodynamics (SPH) method is presented. The proposed framework introduces the concept of modal reference space…
Projection-based model-order reduction via graph autoencoders suited for unstructured meshes
Liam K. Magargal, Parisa Khodabakhshi, Steven N. Rodriguez +2
This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares…