86 citations · 284 across the 9 of their papers we have counts for
5 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…
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Xiaolong He, Qizhi He, Jiun-Shyan Chen
Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the class…
Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials
Xiaolong He, Jiun-Shyan Chen
Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expres…
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…
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…