Loading history and window geometry bound compact-state slip ranking during granular shear startup
Read the original on arXiv Machine Learning →The study investigates granular slip forecasting by separating material state, loading progress, and window geometry effects using a compact neural score based on stress, pressure, coordination, non-affine motion, and force-network observables. Trained on 36 shear trajectories and tested on 18 new ones, the model ranked near‑slip windows better than prevalence or phase controls, yet loading‑history coordinates (e.g., causal elapsed strain) outperformed the compact score. The findings show that while compact observables contain temporally aligned slip information, stronger loading‑history baselines and geometry sensitivity limit the identification of a state‑specific short‑horizon precursor.
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