A computational model for crumpled thin sheets to complement data-driven machine learning

Crumpling is ubiquitous across length scales and diverse structures in nature, yet a complete theoretical description of the mechanisms underlying ridge formation remains elusive. To characterize the intricate damage networks of crumpled thin sheets, our recent work has shown that appropriate simulations can assist data-driven machine learning to overcome the scarcity of high-quality experimental data. Inspired by this data augmentation approach, here we detail a computational model for thin, viscoelastic sheets and demonstrate its ability to capture the properties and behavior of crumpled sheets. We validate the model’s robustness through statistical comparison with high-quality experimental data, and discuss the prospects for its application in assisting data-driven machine learning.