Dresden 2020 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 58: Poster: Nonlinear Dynamics; Pattern Formation; Networks; Delay Systems; Synchronization

DY 58.10: Poster

Thursday, March 19, 2020, 15:00–18:00, P1C

Detection of defects in soft quasicrystals with neural networks — •Ali Döner and Michael Schmiedeberg — Institut für Theoretische Physik I, FAU Erlangen-Nürnberg, Germany

The aim of this work is to employ a neural network for the detection of defects in quasicystalline patterns. Quasircrystals are aperiodic, but they exhibit a longe-range order and in principle can possess any discrete rotational symmetry. We consider quasicrystalline patterns with dodecagonal symmetry as they occur most often in soft matter systems. Our goal is to detect the positions of dislocations as well as their Burgers vector. Our training as well as test data sets consist of calculated patterns with one randomly placed dislocation with one out of six distinguishable Burgers vectors. Our trained neural network is able to recognize the type of the Burgers vector perfectly. The position of the dislocation is recognized up to a mean deviation from the real position of about 0.13 of the small length scale in the quasicrystals. In future, we want to train the network with patterns that contain multiple dislocations as well as phasonic excitations.