Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

Machine learning is a powerful tool for uncovering structure in complex, high-dimensional data. However, a large amount of data is necessary in order to properly train a machine learning network, making it difficult to apply to experimental systems where data is limited. Here we resolve this difficulty by augmenting an experimental dataset with synthetically generated data from a simpler relevant system. Specifically, we study the local order in crease patterns of crumpled sheets, a paradigmatic example of spatial complexity. We supplement sparse crumpled experimental data with abundant simulated sheets of synthetic folds. This technique significantly improves the predictive power in a test problem of pattern completion, demonstrating the usefulness of machine learning in experiments where data may be scarce. Additionally, assessing the accuracy of networks trained with varying types of simulated data reveals the relevance of various physical rules to understanding crease patterns.