A variational physics-informed neural network using Kolosov-Muskhelishvili potentials is introduced for 2D linear elasticity and fracture problems, embedding crack conditions directly into the ansatz.
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Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.
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A Variational Kolosov--Muskhelishvili Network for Elasticity and Fracture
A variational physics-informed neural network using Kolosov-Muskhelishvili potentials is introduced for 2D linear elasticity and fracture problems, embedding crack conditions directly into the ansatz.