English(EN) Loading history and window geometry bound compact-state slip ranking during granular shear startup

新的神经网络模型预测颗粒滑动，但历史和几何形状构成限制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了一种紧凑型神经网络模型，用于预测二维摩擦盘中的颗粒滑动。该模型分析应力、压力、协调性、非仿射运动和力网络可观测值来预测滑动事件。虽然该模型显示出潜力，但其预测能力受到加载历史的影响和事件中心采样几何形状的限制，其中加载历史坐标被证明是更强的预测基线。 AI

影响这项研究可能带来改进的颗粒系统材料行为预测模型，可能影响土木工程和材料科学等领域。

排序理由该条目描述了一篇在arXiv上发表的研究论文，其中详细介绍了一种用于颗粒材料滑动预测的新模型。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Ruixin Zhou, Boliang Yu · 2026-10-03 04:00

加载历史和窗口几何约束了颗粒剪切启动过程中的紧凑状态滑动排名

arXiv:2610.00124v1 Announce Type: cross Abstract: 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…