18 citations · 31 across the 6 of their papers we have counts for
6 papers
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
Jonghyuk Baek, Yanran Wang, J. S. Chen
Conventional finite element methods are known to be tedious in adaptive refinements due to their conformal regularity requirements. Further, the enrichment functions for adaptive r…
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 Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture
Jonghyuk Baek, Jiun-Shyan Chen
Numerical modeling of localizations is a challenging task due to the evolving rough solution in which the localization paths are not predefined. Despite decades of efforts, there i…
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
Yanran Wang, Jonghyuk Baek, Yichun Tang +3
This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from Micro-CT images featuring i…
A Quasi-Conforming Embedded Reproducing Kernel Particle Method for Heterogeneous Materials
Ryan T. Schlinkman, Jonghyuk Baek, Frank N. Beckwith +2
We present a quasi-conforming embedded reproducing kernel particle method (QCE-RKPM) for modeling heterogeneous materials that makes use of techniques not available to mesh-based m…
A Neural Network-enhanced Reproducing Kernel Particle Method for Modeling Strain Localization
Jonghyuk Baek, Jiun-Shyan Chen, Kristen Susuki
Modeling the localized intensive deformation in a damaged solid requires highly refined discretization for accurate prediction, which significantly increases the computational cost…