4 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…
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…
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,…
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…