50 citations · 106 across the 13 of their papers we have counts for
16 papers
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
Jun Sur Richard Park, Auroni Huque Hashim, Siu Wun Cheung +2
Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural network…
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…
Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto +2
Kolmogorov-Arnold Networks (KANs) offer a promising framework for approximating complex nonlinear functions, yet the original B-spline formulation suffers from significant computat…
Model Order Reduction for Quantum Molecular Dynamics
Siu Wun Cheung, Youngsoo Choi, Jean-Luc Fattebert +1
Molecular dynamics simulations are indispensable for exploring the behavior of atoms and molecules. Grounded in quantum mechanical principles, quantum molecular dynamics provides h…
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…
Defining Foundation Models for Computational Science: A Call for Clarity and Rigor
Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim +9
The widespread success of foundation models in natural language processing and computer vision has inspired researchers to extend the concept to scientific machine learning and com…
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…
tLaSDI: Thermodynamics-informed latent space dynamics identification
Jun Sur Richard Park, Siu Wun Cheung, Youngsoo Choi +1
We propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an…
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Christophe Bonneville, Xiaolong He, April Tran +10
Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in…
Data-scarce surrogate modeling of shock-induced pore collapse process
Siu Wun Cheung, Youngsoo Choi, H. Keo Springer +1
Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological science…
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…
AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit's Activation via Adjoint Operators
Qing Wan, Siu Wun Cheung, Yoonsuck Choe
Adjoint operators have been found to be effective in the exploration of CNN's inner workings [1]. However, the previous no-bias assumption restricted its generalization. We overcom…
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…