Abstract

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 toward 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 clustering 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 are published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.

中文翻译：

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通过无监督学习分割机械异构领域

摘要

从生物器官到软机器人，高度可变形材料是自然和工程系统的重要组成部分。这些高度可变形的材料可以具有异质材料特性，并且可以经历具有或不具有潜在材料异质性的异质变形。最近的许多工作已经证实，计算建模方法非常适合理解和预测材料异质性的后果以及解释观察到的异质应变场。特别是，在开发逆分析方法方面已经做出了大量工作，这些方法可以将观察到的运动量（例如位移、应变）转换为材料特性和机械状态。尽管这些方法取得了成功，但它们不一定具有普遍性，并且通常依赖于严格的控制和对边界条件的了解。在这里，我们将利用机器学习方法的最新进展（和普遍性）来探索检测异质材料属性和机械行为模式的替代方法。具体来说，我们将探索无监督学习的聚类和集成聚类方法来识别异质区域。总的来说，我们发现这些方法是有效的，但其能力有限。通过这一初步探索（所有数据和代码均与本手稿一起发布），我们为未来的研究奠定了基础，使这些方法更具体地适应机械数据。