2 citations · 6 across the 14 of their papers we have counts for
15 papers
Latent Space Element Method
Seung Whan Chung, Youngsoo Choi, Christopher Miller +2
How can we build surrogate solvers that train on small domains but scale to larger ones without intrusive access to PDE operators? Inspired by the Data-Driven Finite Element Method…
Sequential decoder training for improved latent space dynamics identification
William Anderson, Seung Whan Chung, Youngsoo Choi
Accurate numerical solutions of partial differential equations are essential in many scientific fields but often require computationally expensive solvers, motivating reduced-order…
Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
Seung Whan Chung, Christopher Miller, Youngsoo Choi +3
Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or disc…
mLaSDI: Multi-stage latent space dynamics identification
William Anderson, Seung Whan Chung, Robert Stephany +1
Accurately solving partial differential equations (PDEs) is essential across many scientific disciplines. However, high-fidelity solvers can be computationally prohibitive, motivat…
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…
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…
Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow
Seung Whan Chung, Youngsoo Choi, Pratanu Roy +6
Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally…
Scaled-up prediction of steady Navier-Stokes equation with component reduced order modeling
Seung Whan Chung, Youngsoo Choi, Pratanu Roy +6
Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predic…
Improved deep learning of chaotic dynamical systems with multistep penalty losses
Dibyajyoti Chakraborty, Seung Whan Chung, Ashesh Chattopadhyay +1
Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of tradition…
A time-parallel multiple-shooting method for large-scale quantum optimal control
N. Anders Petersson, Stefanie Günther, Seung Whan Chung
Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly chal…
Characterization of uncertainties in electron-argon collision cross sections
Seung Whan Chung, Todd A. Oliver, Laxminarayan L. Raja +1
The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the key reaction rates and tr…
Train Small, Model Big: Scalable Physics Simulators via Reduced Order Modeling and Domain Decomposition
Seung Whan Chung, Youngsoo Choi, Pratanu Roy +7
Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pi…
Accelerating Flow Simulations using Online Dynamic Mode Decomposition
Seung Won Suh, Seung Whan Chung, Peer-Timo Bremer +1
We develop an on-the-fly reduced-order model (ROM) integrated with a flow simulation, gradually replacing a corresponding full-order model (FOM) of a physics solver. Unlike offline…
Local Reduced-Order Modeling for Electrostatic Plasmas by Physics-Informed Solution Manifold Decomposition
Ping-Hsuan Tsai, Seung Whan Chung, Debojyoti Ghosh +3
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work,…