SPLIT-PINN infers drift fields in Liouville transport equations from data using marginal corrections and orthogonality constraints to enable probabilistic predictions of microstructural evolution across polycrystal realizations.
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cond-mat.mtrl-sci 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Mesoscale crystal plasticity simulations with GND length-scale hardening capture experimentally observed finite-width adiabatic shear bands and dislocation patterning in polycrystals up to very large strains without softening.
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SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
SPLIT-PINN infers drift fields in Liouville transport equations from data using marginal corrections and orthogonality constraints to enable probabilistic predictions of microstructural evolution across polycrystal realizations.
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Finite-width adiabatic shear banding and dislocation patterning in mesoscale polycrystalline aggregates
Mesoscale crystal plasticity simulations with GND length-scale hardening capture experimentally observed finite-width adiabatic shear bands and dislocation patterning in polycrystals up to very large strains without softening.