102 citations · 787 across the 61 of their papers we have counts for
54 papers · 1 filter
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…
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…
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…
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Justin Baker, Hedi Xia, Yiwei Wang +7
Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers. These solvers are computationally expensive, requiring…
Convex Optimization-Based Structure-Preserving Filter For Multidimensional Finite Element Simulations
Vidhi Zala, Akil Narayan, Robert M Kirby
In simulation sciences, it is desirable to capture the real-world problem features as accurately as possible. Methods popular for scientific simulations such as the finite element…