Dresden 2020 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 33: Granular Matter and Granular Dynamics I

DY 33.7: Talk

Wednesday, March 18, 2020, 11:15–11:30, ZEU 118

Using machine learning to identify variables of a granular theory — •Ansgar Kühn and Matthias Schröter — Max-Planck Institute for Dynamics and Self-organization (MPIDS), Göttingen, Germany

The prediction of contact numbers in granular packings using the local package fraction is described by a theory from [1]. In order to find higher order corrections to that theory, a more detailed descriptions of the local geometry is given by the Minkowski tensors of the Voronoi cell. With this data, machine learning provides a more accurate prediction of contact numbers than [1]. Thus, it can be used to identify new variables relevant for the prediction in order to expand the theory.

[1] Song, C., Wang, P., & Makse, H. A. (2008). Nature, 453, 629.