Fakultät für Mathematik und Naturwissenschaften

MS26: Fractional Dynamics and its Applications

The last two decades brought an impressive growth of interest in fractional dynamics. Mathematical, physical, biological, and medical communities observed that some of the empirical features of various complex systems like long-range memory, non-Markovianity, heavy tails, super- and sub-linear mean square displacement cannot be described accurately by classical diffusion processes. Therefore, new models and methods connected with anomalous diffusion processes have been successfully introduced and applied to modelling of real-world phenomena. This minisymposium will bring together researchers working on the proposed topics and their interface, fostering a cross-pollination between different communities.

Tuesday, April 13, 16:00-17:40 (Chair: Aleksander Weron/Krzysztof Burnecki)

  1. 16:00 Diego Krapf, Anomalous Diffusion, Ergodicity Breaking, and Ageing for Single-Particle Tracking in Biological Membranes
  2. 16:25 Joanna Janczura, Fractional Processes with Switching: Application to Cell Dynamics and Solar Flares Modeling
  3. 16:50 Jakub Ślęzak, Fractional Diffusion Stemming from Heterogeneity
  4. 17:15 Janusz Szwabiński, Miłosz Gajowczyk, Classification of Diffusion Modes with Deep Learning Methods

Thursday, April 15, 10:20-12:00 (Chair: Marcin Magdziarz/Agnieszka Wyłomańska)

  1. 10:20 Gianni Pagnini, Silvia Vitali, Should I Stay or Should I Go? Zero-Size Jumps in Random Walks for Lévy Flights
  2. 10:45 Łukasz Płociniczak, Numerical Method for the Time-Fractional Porous Medium Equation
  3. 11:10 Janusz Gajda, Fractional Differentiation in Financial Modelling
  4. 11:35 Michal Balcerek, Krzysztof Burnecki, Testing of Multifractional Brownian Motion

Thursday, April 15, 14:30-16:10 (Chair: Marek Teuerle/Janusz Szwabiński)

  1. 14:30 Katarzyna Gabriela Maraj, Empirical Anomaly Measure in Application to Testing of Anomalous Diffusion Behavior
  2. 14:55 Dawid Szarek, Neural Networks for Fractional Dynamic Identification
  3. 15:20 Patrycja Kowalek, Machine Learning vs Statistical Testing Hypothesis Approaches for Fractional Anomalous Diffusion Classification

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