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
3D in-situ imaging shows cross-slip enabling dislocations to escape pile-ups in pure aluminum, producing intermittent plastic flow during tensile deformation.
citing papers explorer
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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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Revealing Dislocation Interactions Controlling Mechanical Properties of Metals
3D in-situ imaging shows cross-slip enabling dislocations to escape pile-ups in pure aluminum, producing intermittent plastic flow during tensile deformation.