A PINN surrogate, seeded with a generalized-gamma ansatz, maps fragmentation-model parameters (alpha,gamma) directly to the scaled fragment-size density and is shown to beat a finite-difference solver in speed while matching exact solutions at benchmark parameters.
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Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
A PINN surrogate, seeded with a generalized-gamma ansatz, maps fragmentation-model parameters (alpha,gamma) directly to the scaled fragment-size density and is shown to beat a finite-difference solver in speed while matching exact solutions at benchmark parameters.