35 citations · 55 across the 8 of their papers we have counts for
8 papers
NN-OpInf: an operator inference approach using structure-preserving composable neural networks
Eric Parish, Anthony Gruber, Patrick Blonigan +1
We propose neural network operator inference (NN-OpInf): a structure-preserving, composable, and minimally restrictive operator inference framework for the non-intrusive reduced-or…
Kernel manifolds: nonlinear-augmentation dimensionality reduction using reproducing kernel Hilbert spaces
Alejandro N. Diaz, Jacob T. Needels, Irina K. Tezaur +1
This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs,…
Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces
Alejandro N Diaz, Shane A McQuarrie, John T Tencer +1
This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynam…
Physics-Infused Reduced-Order Modeling for Analysis of Multi-Layered Hypersonic Thermal Protection Systems
Carlos A. Vargas Venegas, Daning Huang, Patrick Blonigan +1
This work presents a physics-infused reduced-order modeling (PIROM) framework for efficient and accurate prediction of transient thermal behavior in multi-layered hypersonic therma…
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…
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…
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems
Francesco Rizzi, Patrick J. Blonigan, Eric J. Parish +1
This work introduces Pressio, an open-source project aimed at enabling leading-edge projection-based reduced order models (ROMs) for large-scale nonlinear dynamical systems in scie…
Are extreme dissipation events predictable in turbulent fluid flows?
Patrick J. Blonigan, Mohammad Farazmand, Themistoklis P. Sapsis
We derive precursors of extreme dissipation events in a turbulent channel flow. Using a recently developed method that combines dynamics and statistics for the underlying attractor…