A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
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Elliptic energy loses coercivity on neural ansatzes due to manifold non-closedness and condensation, but state functions remain bounded and converge strongly, with rates proved for Gaussian wave-packet approximations.
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Deep learning-based phase-field modelling of brittle fracture in anisotropic media
A variational physics-informed neural network solves higher-order anisotropic phase-field fracture models by minimizing total energy with B-spline enriched trial functions.
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The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence
Elliptic energy loses coercivity on neural ansatzes due to manifold non-closedness and condensation, but state functions remain bounded and converge strongly, with rates proved for Gaussian wave-packet approximations.