102 citations · 787 across the 61 of their papers we have counts for
70 papers
Greedy Rational Approximation for Frequency-Domain Model Reduction of Parametric LTI Systems
Filip Bělík, Yanlai Chen, Akil Narayan
We investigate model reduction of parametric linear time-invariant (LTI) dynamical systems. When posed in the frequency domain, this problem can be formulated as seeking a low-orde…
Optimally balancing exploration and exploitation to automate multi-fidelity statistical estimation
Thomas Dixon, Alex Gorodetsky, John Jakeman +2
Multi-fidelity methods that use an ensemble of models to compute a Monte Carlo estimator of the expectation of a high-fidelity model can significantly reduce computational costs co…
TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
Yanlai Chen, Yajie Ji, Akil Narayan +1
We introduce the Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN) for accomplishing nonlinear model order reduction (MOR) of transport-dominated part…
An approximate control variates approach to multifidelity distribution estimation
Ruijian Han, Boris Kramer, Dongjin Lee +2
Forward simulation-based uncertainty quantification that studies the distribution of quantities of interest (QoI) is a crucial component for computationally robust engineering desi…
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Nuojin Cheng, Osman Asif Malik, Yiming Xu +3
Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space explorat…
Fast algorithms for least square problems with Kronecker lower subsets
Osman Asif Malik, Yiming Xu, Nuojin Cheng +3
While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits p…