34 citations · 71 across the 6 of their papers we have counts for
7 papers
Certified data-driven physics-informed greedy auto-encoder simulator
Xiaolong He, Youngsoo Choi, William D. Fries +2
A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-en…
Local Lagrangian reduced-order modeling for Rayleigh-Taylor instability by solution manifold decomposition
Siu Wun Cheung, Youngsoo Choi, Dylan Matthew Copeland +1
Rayleigh-Taylor instability is a classical hydrodynamic instability of great interest in various disciplines of science and engineering, including astrophyics, atmospheric sciences…
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg +4
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inve…
Efficient nonlinear manifold reduced order model
Youngkyu Kim, Youngsoo Choi, David Widemann +1
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dim…
Component-wise reduced order model lattice-type structure design
Sean McBane, Youngsoo Choi
Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely o…
Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems
Youngsoo Choi, Peter Brown, Bill Arrighi +1
A classical reduced order model for dynamical problems involves spatial reduction of the problem size. However, temporal reduction accompanied by the spatial reduction can further…