SKM 2021 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 4: Poster Session II: Nonlinear Dynamics, Simulations and Machine Learning
DY 4.5: Poster
Dienstag, 28. September 2021, 17:30–19:30, P
Neural Network-Based Approaches for Multiscale Modelling of Topological Defects — •Kyra Klos1, Karin Everschor-Sitte2, and Friederike Schmid1 — 1Johannes Gutenberg University, Mainz, Germany — 2University of Duisburg-Essen, Duisburg, Germany
Topological defects and their dynamics are a heavily researched topic in a wide range of physics fields.[1]
Due to the multiscale character of those defect structures, numerically simulating a large number of them in full microscopic detail gets highly complicated, as the large size of associated deformation fields around each core leads to a complex interaction pattern.
To give a possible insight into the connection between the macroscopic (particle) description of a model with topological defects and the underlying microscopic true structure, the use of neural networks is proposed. Starting with a spin-dynamic simulated microscopic model as input [2,3], a fully convolutional network (FCN)[4] is used to simplify the complex defect structure of the microscopic theory without loss of valuable information. This allows the extraction of the configuration and location of the topological defects.
[1] Mermin, N. D., Rev. Mod. Phys. 51, 591, (1979)
[2] Leoncini, X. et. al., Phys. Rev. E 57(6), 6377, (1998)
[3] Cerruti-Sola, M. et. al., Phys. Rev. E 61(5A), 5171, (2000)
[4] Long, J. et. al., IEEE, 39(4), 640, (2017)