50 citations · 106 across the 13 of their papers we have counts for
8 papers · 1 filter
A Reduced Order Model approach for First-Principles Molecular Dynamics Computations
Siu Wun Cheung, Youngsoo Choi, Jean-Luc Fattebert +2
To leverage the redundancy between the electronic structure computed at each step of first-principles molecular dynamics, we present a data-driven modeling framework for Kohn-Sham…
Machine-precision energy conservative reduced models for Lagrangian hydrodynamics by quadrature methods
Chris Vales, Siu Wun Cheung, Dylan M. Copeland +1
We present an energy conservative, quadrature based model reduction framework for the compressible Euler equations of Lagrangian hydrodynamics. Building on a finite element discret…
Theory and numerics of subspace approximation of eigenvalue problems
Siu Wun Cheung, Youngsoo Choi, Seung Whan Chung +3
Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approx…
S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models
Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin +3
While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order di…
Reduced order models for Lagrangian hydrodynamics
Dylan Matthew Copeland, Siu Wun Cheung, Kevin Huynh +1
As a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-domi…
Multiscale simulations for upscaled multi-continuum flows
Jun Sur Richard Park, Siu Wun Cheung, Tina Mai +1
We consider in this paper a challenging problem of simulating fluid flows, in complex multiscale media possessing multi-continuum background. As an effort to handle this obstacle,…
Dynamic Data-driven Bayesian GMsFEM
Siu Wun Cheung, Nilabja Guha
In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabili…
Bayesian Multiscale Finite Element Methods. Modeling missing subgrid information probabilistically
Y. Efendiev, W. T. Leung, S. W. Cheung +3
In this paper, we develop a Bayesian multiscale approach based on a multiscale finite element method. Because of scale disparity in many multiscale applications, computational mode…