8 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…
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…
Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks
Axel Larsson, Minji Kim, Chris Vales +4
Hyper-reduction methods have gained increasing attention for their potential to accelerate reduced order models for nonlinear systems, yet their comparative accuracy and computatio…
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…