86 citations · 284 across the 9 of their papers we have counts for
10 papers
Thermodynamically Consistent Machine Learning for Fracture Prediction in Heterogeneous Materials
Muhamad Alim, Xiaolong He, Mei-Na Tsao +2
Understanding fracture behavior is critical for assessing the structural integrity and longevity. Highly time-consuming physics-based fracture simulations motivate the development…
Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions
Karan Taneja, Xiaolong He, Chung-Hao Lee +4
Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Tr…
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
Jonghyuk Baek, Yanran Wang, Xiaolong He +3
In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 °C. In this range of extrem…
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
Karan Taneja, Xiaolong He, Qizhi He +1
This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identificatio…
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…
Long time stability of KAM tori for the nonlinear Schrödinger equation
Xiaolong He, Jia Shi, Xiaoping Yuan
In this paper, we prove the long time stability of KAM tori for the nonlinear Schrödinger equation on the torus with arbitrary dimensions.