most citedA Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations

11 citations · 14 across the 3 of their papers we have counts for

collaborators

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★ 1 cited

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★ 11 cited

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.…