From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work towards developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clutering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code is published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.

翻译：从生物器官到软体机器人，高度可变形材料是自然与工程系统的关键组成部分。这些材料可能具有异质力学属性，且无论是否存在材料异质性，都可能经历异质性变形。近期诸多研究证实，计算建模方法适用于理解与预测材料异质性的影响，以及解释观测到的异质应变场。尤其值得注意的是，在开发能够将观测运动学量（如位移、应变）转化为材料属性与力学状态的反向分析方面，已有重要进展。尽管此类方法取得了成功，但其通用性有限，通常依赖于对边界条件的严格控制和认知。本文基于机器学习方法的最新进展（及其普及性），探索从异质材料属性与力学行为中检测模式的替代方案。具体而言，我们将探索基于无监督学习的聚类与集成聚类方法识别异质区域。总体而言，这些方法虽有效但其能力存在局限性。通过这项初步探索（所有数据与代码均随本文公开），我们为后续更具体适配力学数据的方法研究奠定了基础。