252 citations · 365 across the 6 of their papers we have counts for
5 papers · 1 filter
Residual-Driven Lifting Identification for Nonlinear-Manifold Reduced-Order Models of Parametrized Linear PDEs
Francesco A. B. Silva, Jean C. Ragusa, Theron Guo +1
We introduce a residual-driven procedure for training nonlinear-manifold reduced-order models for parametrized linear partial differential equations that, given prescribed latent a…
Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen, Laura Balzano, Stephen Wright +1
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the d…
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
Rudy Geelen, Laura Balzano, Karen Willcox
We present a novel framework for learning cost-efficient latent representations in problems with high-dimensional state spaces through nonlinear dimension reduction. By enriching l…
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
Harsh Sharma, Hongliang Mu, Patrick Buchfink +3
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical symplectic model…
Operator inference for non-intrusive model reduction with quadratic manifolds
Rudy Geelen, Stephen Wright, Karen Willcox
This paper proposes a novel approach for learning a data-driven quadratic manifold from high-dimensional data, then employing this quadratic manifold to derive efficient physics-ba…