Jammed soft disks exhibit avalanches of particle rearrangements under quasistatic shear. We introduce a framework for understanding the statistics of the progression of avalanches. We follow the avalanches (simulated using steepest descent energy minimization) to decompose them into individual localized rearrangements. We characterize the local structural environment of each particle by a machine-learned quantity, softness, designed to be highly correlated with rearrangements, and analyze the interplay between softness, rearrangements, and strain. Local yield strain has long been incorporated into elastoplastic models; here we show that softness provides a useful proxy for local yield strain. Our findings demonstrate that elastoplastic models must take into account the fully tensorial strain field in order to include the effects of changes in local yield strain due to rearrangements and introduce the equations underpinning a structuro-elastoplastic model that includes local softness.

中文翻译：

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雪崩传播过程中重排、应变和局部结构的相互作用

卡住的软盘在准静态剪切下表现出粒子重排的雪崩。我们引入了一个框架来理解雪崩进展的统计数据。我们遵循雪崩（使用最速下降能量最小化模拟）将它们分解为单独的局部重排。我们通过机器学习的数量、软度来表征每个粒子的局部结构环境，设计为与重排高度相关，并分析软度、重排和应变之间的相互作用。局部屈服应变早已被纳入弹塑性模型；在这里，我们表明柔软度为局部屈服应变提供了有用的代理。