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Found 5 papers, 0 papers with code
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
Conventional finite element methods are known to be tedious in adaptive refinements due to their conformal regularity requirements.
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 {\deg}C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydro-mechanical (THM) behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency.
A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture
In the proposed method, a background reproducing kernel (RK) approximation defined on a coarse and uniform discretization is enriched by a neural network (NN) approximation under a Partition of Unity framework.
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
The proposed method modifies the smooth kernel functions with a regularized heavy-side function concerning the material interfaces to alleviate Gibb's oscillations.
A Neural Network-enhanced Reproducing Kernel Particle Method for Modeling Strain Localization
In this work, neural network-enhanced reproducing kernel particle method (NN-RKPM) is proposed, where the location, orientation, and shape of the solution transition near a localization is automatically captured by the NN approximation via a block-level neural network optimization.
