A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.
Computational micro-to-macro transitions of discretized microstructures undergoing small strains.Archive of Applied Mechanics, 72(4):300–317, 2002
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Neural Operator Representation of Granular Micromechanics-based Failure Envelope
A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.