2 citations · 3 across the 5 of their papers we have counts for
6 papers
Higher-Order LaSDI: Reduced Order Modeling with Multiple Time Derivatives
Robert Stephany, William Michael Anderson, Youngsoo Choi
Solving complex partial differential equations is vital in the physical sciences, but often requires computationally expensive numerical methods. Reduced-order models (ROMs) addres…
Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition for Reduced Order Modeling of Radiation Transport
Quincy Huhn, Jean Ragusa, Youngsoo Choi
Deterministic solutions to the Sn transport equation can be computationally expensive to calculate. Reduced Order Models (ROMs) provide an efficient means of approximating the Full…
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…
Scalable nonlinear manifold reduced order model for dynamical systems
Ivan Zanardi, Alejandro N. Diaz, Seung Whan Chung +2
The domain decomposition (DD) nonlinear-manifold reduced-order model (NM-ROM) represents a computationally efficient method for integrating underlying physics principles into a neu…
Nonlinear-manifold reduced order models with domain decomposition
Alejandro N. Diaz, Youngsoo Choi, Matthias Heinkenschloss
A nonlinear-manifold reduced order model (NM-ROM) is a great way of incorporating underlying physics principles into a neural network-based data-driven approach. We combine NM-ROMs…
A variant of the Brillouin-Wigner perturbation theory with Epstein-Nesbet partitioning
Sangyoub Lee, Cheol Ho Choi, Eunji Kim +1
We present an elementary pedagogical derivation of the Brillouin-Wigner and the Rayleigh-Schrödinger perturbation theories with Epstein-Nesbet partitioning. A variant of the Brillo…