137 citations · 267 across the 11 of their papers we have counts for
6 papers · 1 filter
Fast Quadratic Manifold Learning For Nonlinear Dimensionality Reduction in Large-scale Systems using Riemannian Optimization
Gavin Paxton, Seunghee Cheon, Rudy Geelen +1
The effectiveness of dimensionality reduction with quadratic manifolds hinges on the choice of a reduced basis and the associated quadratic correction terms. Existing approaches ty…
A Dynamic Subspace Approach for Low-rank Approximation of Large-scale Nonlinear Systems
Jack DeChant, Rudy Geelen, Shane A. McQuarrie +1
We present a dynamic subspace approach for efficiently approximating large-scale systems by learning time-continuous trajectories on the Grassmannian manifold. By parameterizing a…
Tensor parametric Hamiltonian operator inference
Arjun Vijaywargiya, Shane A. McQuarrie, Anthony Gruber
This work presents a tensorial approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian st…
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems
Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox +1
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use…
Bayesian operator inference for data-driven reduced-order modeling
Mengwu Guo, Shane A. McQuarrie, Karen E. Willcox
This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning…
Concurrent multi-parameter learning demonstrated on the Kuramoto-Sivashinsky equation
Benjamin Pachev, Jared P. Whitehead, Shane A. McQuarrie
We develop an algorithm based on the nudging data assimilation scheme for the concurrent (on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative parti…