Using Machine Learning to Understand the Evolution of Damage Networks in Thin Sheets

The evolution of creases in a repeatedly crumpled thin sheet is incredibly complex. Nevertheless, some characteristics of this system have recently been shown to be very simple, such as the ability of total crease length to describe the evolution of damage via the equation of state. Yet, the equation of state cannot predict important properties such as when and where new creases will appear. Machine learning may be a good candidate for uncovering such hidden spatial-temporal patterns. However, systematic analysis of complex systems requires the appropriate alliance of inherently data-limited lab experiments with the big-data nature of machine learning. Thus, I will discuss aspects of how the inclusion of machine learning into the data analysis modifies how the experiment must be set up and how to develop new strategies for acquisition of data.