论文标题
通过数据分析将可塑性与位错属性有关：缩放与机器学习方法
Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches
论文作者
论文摘要
长期以来，可塑性建模一直基于基于本构关系的临时爆发的现象学模型，然后将其拟合到有限的数据中。其他工作是基于对物理机制的考虑，该机制试图通过鉴定孤立的缺陷过程（“机制”）来建立观察到的塑性变形行为的物理基础，这些缺陷过程是在实验或模拟中观察到的，然后用于制定所谓的物理模型。这些方法都不足以捕获属于新兴集体现象领域的塑性变形的复杂性，并了解现代高性能结构材料的核心多重变形途径的复杂相互作用。基于数据的方法为可塑性建模提供了替代途径，我们在此处探索了一个简单的示例，即速率和位错密度依赖于FCC金属中的增强机制之间的相互作用。
Plasticity modelling has long been based on phenomenological models based on ad-hoc assuption of constitutive relations, which are then fitted to limited data. Other work is based on the consideration of physical mechanisms which seek to establish a physical foundation of the observed plastic deformation behavior through identification of isolated defect processes ('mechanisms') which are observed either experimentally or in simulations and then serve to formulate so-called physically based models. Neither of these approaches is adequate to capture the complexity of plastic deformation which belongs into the realm of emergent collective phenomena, and to understand the complex interplay of multiple deformation pathways which is at the core of modern high performance structural materials. Data based approaches offer alternative pathways towards plasticity modelling whose strengths and limitations we explore here for a simple example, namely the interplay between rate and dislocation density dependent strengthening mechanisms in fcc metals.
