When a sheet of paper is crumpled, it spontaneously develops a network of creases. Despite the apparent disorder of this process, statistical properties of crumpled sheets exhibit striking reproducibility. Recent experiments have shown that when a sheet is repeatedly crumpled, the total crease length grows logarithmically . This talk will offer insight into this surprising result by developing a correspondence between crumpling and fragmentation processes. We show how crumpling can be viewed as fragmenting the sheet into flat facets that are outlined by the creases, and we use this model to reproduce the characteristic
logarithmic scaling of total crease length, thereby supplying a missing physical basis for the observed phenomenon .
This study was made possible by large-scale data analysis of crease networks from crumpling experiments. We will describe recent work to use the same data with machine learning methods to probe the physical rules governing crumpling. We will look at how augmenting experimental data with synthetically generated data can improve predictive power and provide physical insight .
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Bio: Chris Rycroft is the John L. Loeb Associate Professor of Engineering and Applied Sciences in the Harvard John A. Paulson School of Engineering and Applied Sciences. He is interested in mathematical modeling and scientific computation for interdisciplinary applications in science and engineering. At Harvard, he is involved in several centers promoting interdisciplinary research, including the Quantitative Biology Initiative, the Materials Research Science and Engineering Center, and the Institute for Applied Computational Science. He is a 2021 recipient of the Everett Mendelsohn Award, a Harvard-wide award for excellence in graduate mentoring. Prior to his appointment at Harvard, Rycroft was a Morrey Assistant Professor in the Department of Mathematics at the University of California, Berkeley. Rycroft is a visiting faculty scientist at the Lawrence Berkeley Laboratory, where he has worked on several projects relating to energy production and efficiency. He obtained his Ph.D. in Mathematics in 2007 from the Massachusetts Institute of Technology.