发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过紧凑神经评分分离颗粒剪切中的材料状态与加载历史，发现加载历史坐标预测更强，但窗口几何限制证据，不支持特定状态短时前兆。
AI 中文摘要
颗粒滑移预测可能混淆材料状态、加载进程以及以事件为中心的采样几何。我们通过一个包含应力、压力、配位数、非仿射运动和力网络观测量的紧凑神经评分，在缓慢剪切的二维摩擦圆盘中分离了这些贡献。该模型在36条轨迹上开发，并在18条新轨迹上于两种嵌套的应力降定义下进行冻结测试。对留出结果的检查揭示了事件后采样不对称性；因此，恢复感知分析是描述性的。在轨迹等权的情况下，紧凑评分在两种定义下均将近滑移窗口排在患病率和轨迹内圆相位对照之上（代表性平均精度0.310对患病率0.173；相位零假设上限0.257）。加载历史坐标排名更强，因果累积应变达到0.534。恢复感知规则保留了78.4%的活动门控事件，并优先选择较长的先前间隔；按自上次编目事件以来的时间排序仍与计数条件几何零假设兼容。因此，紧凑观测量包含时间对齐的滑移信息，但更强的加载历史基线和窗口几何敏感性限制了这一证据。这些启动数据不能隔离出超出加载历史的特定状态短时前兆，也不支持累积应变排序的更新解释。
英文摘要
Granular slip forecasting can conflate material state, loading progress, and the geometry of event-centered sampling. We separated these contributions in slowly sheared two-dimensional frictional disks using a compact neural score of stress, pressure, coordination, non-affine motion, and force-network observables. The model was developed on 36 trajectories and frozen efore testing on 18 new trajectories under two nested stress-drop definitions. Inspection of held-out results revealed post-event sampling asymmetry; recovery-aware analyses are therefore descriptive. With trajectories weighted equally, the compact score ranked near-slip windows above both prevalence and within-trajectory circular-phase controls under both definitions (representative average precision 0.310 versus prevalence 0.173; phase-null upper bound 0.257). Loading-history coordinates ranked more strongly, reaching 0.534 for causal elapsed strain. The recovery-aware rule retained 78.4\% of activity-gated events and preferentially selected longer preceding intervals; ranking by time since the previous catalogued event remained compatible with a count-conditioned geometry null. Compact observables thus contain temporally aligned slip information, but stronger loading-history baselines and window-geometry sensitivity bound that evidence. These startup data do not isolate a state-specific short-horizon precursor beyond loading history or support a renewal interpretation of elapsed-strain ranking.
Comments28 pages, 6 figures
