Segmenting mechanically heterogeneous domains via unsupervised learning
Quan Nguyen Emma Lejeune
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.