2 citations · 4 across the 11 of their papers we have counts for
11 papers
Reference-Space Reuse for Steady and Transient State-Constrained Conjugate Heat Transfer
Teeratorn Kadeethum, Francesco Ballarin, Youngsoo Choi +1
Repeated thermal optimization must enforce local temperature limits while solving linear systems whose unknowns change as the constraints activate and release. Zero-extension trans…
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…
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 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…
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…
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
Christophe Bonneville, Youngsoo Choi, Debojyoti Ghosh +1
Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs). We…
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,…