2 citations · 2 across the 1 of their papers we have counts for
3 papers
physics.comp-ph2023★ 2 cited
Hyper-Reduced Autoencoders for Efficient and Accurate Nonlinear Model Reductions
Jorio Cocola, John Tencer, Francesco Rizzi +2
Projection-based model order reduction on nonlinear manifolds has been recently proposed for problems with slowly decaying Kolmogorov n-width such as advection-dominated ones. Thes…
physics.comp-ph2020
A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems
Francesco Rizzi, Eric J. Parish, Patrick J. Blonigan +1
This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practic…
physics.comp-ph2020
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations
John Tencer, Kevin Potter
We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries.…