208 citations · 894 across the 25 of their papers we have counts for
27 papers
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
Christophe Bonneville, Youngsoo Choi, Debojyoti Ghosh +1
Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are…
Data-scarce surrogate modeling of shock-induced pore collapse process
Siu Wun Cheung, Youngsoo Choi, H. Keo Springer +1
Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological science…
A fast and accurate domain-decomposition nonlinear manifold reduced order model
Alejandro N. Diaz, Youngsoo Choi, Matthias Heinkenschloss
This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear-manifold…
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…
Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities
Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry +3
Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations were per…
Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models
Sean McBane, Youngsoo Choi, Karen Willcox
Lattice-like structures can provide a combination of high stiffness with light weight that is useful in many applications, but a resolved finite element mesh of such structures res…
Parametric Dynamic Mode Decomposition for Reduced Order Modeling
Quincy A. Huhn, Mauricio E. Tano, Jean C. Ragusa +1
Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. Th…
gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification
Xiaolong He, Youngsoo Choi, William D. Fries +2
A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modelin…
S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models
Jessica T. Lauzon, Siu Wun Cheung, Yeonjong Shin +3
While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order di…
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Teeratorn Kadeethum, Francesco Ballarin, Daniel O'Malley +3
We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-…
LaSDI: Parametric Latent Space Dynamics Identification
William Fries, Xiaolong He, Youngsoo Choi
Enabling fast and accurate physical simulations with data has become an important area of computational physics to aid in inverse problems, design-optimization, uncertainty quantif…
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…
Reduced order models for Lagrangian hydrodynamics
Dylan Matthew Copeland, Siu Wun Cheung, Kevin Huynh +1
As a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-domi…
Efficient space-time reduced order model for linear dynamical systems in Python using less than 120 lines of code
Youngkyu Kim, Karen May Wang, Youngsoo Choi
A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space-time ROM for linear dynamical…
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…
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
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…
Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction
Chi Hoang, Youngsoo Choi, Kevin Carlberg
A novel domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations (e.g., arising from…
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…
Accelerating design optimization using reduced order models
Youngsoo Choi, Geoffrey Oxberry, Daniel White +1
Although design optimization has shown its great power of automatizing the whole design process and providing an optimal design, using sophisticated computational models, its proce…
SNS: A Solution-based Nonlinear Subspace method for time-dependent model order reduction
Youngsoo Choi, Deshawn Coombs, Robert Anderson
Several reduced order models have been developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational c…
Conservative model reduction for finite-volume models
Kevin Carlberg, Youngsoo Choi, Syuzanna Sargsyan
This work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsi…
Space-time least-squares Petrov-Galerkin projection for nonlinear model reduction
Youngsoo Choi, Kevin Carlberg
This work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model…