137 citations · 267 across the 11 of their papers we have counts for
12 papers
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…
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri
This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation o…
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…
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…
Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference
Shane A McQuarrie, Parisa Khodabakhshi, Karen E Willcox
This work formulates a new approach to reduced modeling of parameterized, time-dependent partial differential equations (PDEs). The method employs Operator Inference, a scientific…
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…
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
Shane A. McQuarrie, Cheng Huang, Karen E. Willcox
This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven lear…
Data Assimilation in Large-Prandtl Rayleigh-Bénard Convection from Thermal Measurements
A. Farhat, N. E. Glatt-Holtz, V. R. Martinez +2
This work applies a continuous data assimilation scheme---a particular framework for reconciling sparse and potentially noisy observations to a mathematical model---to Rayleigh-Bén…
Volume and geometry of homogeneously adequate knots
Paige Bartholomew, Shane McQuarrie, Jessica S. Purcell +1
We bound the hyperbolic volumes of a large class of knots and links, called homogeneously adequate knots and links, in terms of their diagrams. To do so, we use the decomposition o…