A Kolosov-Muskhelishvili informed neural network satisfies plane elasticity equations by construction, achieves sub-1% errors on benchmarks, and uses transfer learning to predict crack paths under multiple criteria with over 70% less training time.
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Separate Puck fibre and matrix/inter-fibre phase-field fatigue channels, degrading resistance not stiffness, reproduce orientation- and notch-dependent UD composite fatigue modes with one fixed card.
citing papers explorer
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Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics
A Kolosov-Muskhelishvili informed neural network satisfies plane elasticity equations by construction, achieves sub-1% errors on benchmarks, and uses transfer learning to predict crack paths under multiple criteria with over 70% less training time.
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A Puck-informed mode-resolved phase-field fatigue framework for unidirectional composites
Separate Puck fibre and matrix/inter-fibre phase-field fatigue channels, degrading resistance not stiffness, reproduce orientation- and notch-dependent UD composite fatigue modes with one fixed card.