Research Field

Topological defects are stable localized perturbations of an underlying ordering field characterized by their winding number. These microscopic structures exhibit an intrinsic multi-scale character and can be described as point-like quasi-particles in the macroscopic picture.

Since they have widespread applications and theoretical significance, simulations are of high interest yet difficult to realize for large system sizes. The difficulties arise from long-range interaction patterns and the intricate multiscale implications, such as the Berezinskii-Kosterlitz-Thouless phase transition.

In my research I am using physics induced generative neural networks, for example a Wasserstein Generative Adversarial Networks (WGAN), to bridge the scales and overcome the limitations of traditional simulations.

Another part of my research focuses on the characterization of topological defects in a global manner using techniques of topological data analysis such as persistent homology as well as graph representation modelling.

By leveraging these cutting-edge tools, I aim to open new pathways for exploring and understanding the complex dynamics of topological defects.