208 citations · 894 across the 25 of their papers we have counts for
9 papers · 1 filter
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…