Predicting the dynamics of crumpling with machine learning
ORAL
Abstract
The simple process of crumpling a sheet of paper with our hands results in a complex network of interconnected permanent creases of many sizes and orientations. On subsequent crumples, the sheet preferentially bends along these creases, introducing history dependence to the process. Here, we study the dynamics of crumpling using machine learning methods to analyze the complex crease networks. We perform experiments to systematically apply successive crumples to a sheet, from which we extract the crease networks using high resolution scans of mean curvature. We use the scans as input to machine learning methods to ask questions such as predicting hot spots for further crease generation. We discuss broader questions about how best to design experiments to take advantage of machine learning approaches.
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Presenters
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Christopher Rycroft
- SEAS, Harvard Univ
- Harvard University
- SEAS, Harvard University
- John A. Paulson School of Engineering and Applied Sciences, Harvard University
- Harvard Univ
- Paulson School of Engineering and Applied Sciences, Harvard University
- Applied Mathematics, Harvard University