A variational neural network using Kolosov-Muskhelishvili potentials solves 2D linear elasticity and fracture problems by minimizing total potential energy and embedding crack discontinuities into the ansatz, yielding higher accuracy and faster convergence than standard physics-informed networks.
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Stochastic material heterogeneity modeled with Gaussian random fields in a nonlocal framework fundamentally changes phase nucleation, localization, and macroscopic mechanical response in architected metamaterials.
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A Variational Kolosov--Muskhelishvili Network for Elasticity and Fracture
A variational neural network using Kolosov-Muskhelishvili potentials solves 2D linear elasticity and fracture problems by minimizing total potential energy and embedding crack discontinuities into the ansatz, yielding higher accuracy and faster convergence than standard physics-informed networks.
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Influence of Heterogeneity on the Response of Architected Metamaterials
Stochastic material heterogeneity modeled with Gaussian random fields in a nonlocal framework fundamentally changes phase nucleation, localization, and macroscopic mechanical response in architected metamaterials.