Lokální interakce a korelace v komplexních systémech
| Thesis title in Czech: | Lokální interakce a korelace v komplexních systémech |
|---|---|
| Thesis title in English: | Understanding collective behavior based on long-ranged correlations |
| Academic year of topic announcement: | 2022/2023 |
| Thesis type: | dissertation |
| Thesis language: | čeština |
| Department: | Department of Macromolecular Physics (32-KMF) |
| Supervisor: | RNDr. Artem Ryabov, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 26.09.2022 |
| Date of assignment: | 26.09.2022 |
| Confirmed by Study dept. on: | 26.09.2022 |
| Advisors: | RNDr. Viktor Holubec, Ph.D. |
| Guidelines |
| Principal tasks:
- Studying existing literature on theoretical particle-based stochastic modeling of natural living systems and corresponding experiments addressing their collective behavior. - Investigating theoretically which types of local interactions among organisms might lead to maximization of long-range correlations. - Confronting theoretical results with the behavior of groups of organisms observed in nature. - Development of analytical methods (approximative perturbation theories; effective model systems) and numerical algorithms (Monte Carlo and Brownian dynamics simulations of systems of interacting particles); in computationally extensive cases, implementing machine learning methods to assess the resulting interactions. The topic of this dissertation thesis intersects with a research plan of the Primus grant project "Do long-range correlations determine social interactions in many body living systems?" awarded to by Dr. V. Holubec (co-advisor). The student will be employed to work on this project. The research will be carried out in collaboration with Prof. Klaus Kroy (theory) and Prof. Frank Cichos (experiment) from the University of Leipzig, Germany. |
| References |
| [1] A Cavagna, I Giardina, and TS Grigera, Phys. Rep. 728, 1 (2018)
[2] U Khadka, V Holubec, H Yang, and F Cichos, Nat. Commun. 9, 3864 (2018) [3] A Ryabov and M Tasinkevych, Soft Matter, accepted (2022), DOI: 10.1039/D2SM00054G [4] V Holubec, A Ryabov, SAM Loos, K Kroy, New J. Phys. 24 023021 (2022) [5] HJ Charlesworth and MS Turner, PNAS 116 (31), 15362 (2019) [6] M Durve, F Peruani, and A Celani, Phys. Rev. E 102, 012601 (2020) [7] S Muiños-Landin, A Fischer, V Holubec, and F Cichos, Sci. Robot. 6, 52 (2021) [8] CW Gardiner, Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences (2nd ed., Springer-Verlag Berlin 1983) [9] Další odborná časopisecká literatura dle doporučení školitele |
| Preliminary scope of work |
| Natural complex systems like neurons in the brain, bacteria colonies, groups of insects, and even flocks of birds, are known to exhibit long-range correlations. Their presence is frequently explained by means of to the concept of self-organized criticality and thus their theoretical investigations and modeling are usually focused on verification and classification of the emergent critical behavior. In this thesis, we shall focus on a different perspective. Our main assumption will be that individuals, which would like to develop a collective behavior within their group, must build long-ranged correlations, which would allow effective communication within the whole group. This assumption will determine the types of interactions between agents. We aim to test the hypothesis that optimization of correlations between a given set of agents as a function of their interactions can yield the observed group behavior. Results of this research will help to understand how interactions in groups of living agents arise and adapt to changing environments. They will be also useful in designing optimal control of groups of artificial robots. Interactions predicted for the system of active Brownian particles will be realized and tested experimentally in a real-world environment.
Required skills of the candidate: - Experience with and basic knowledge of mathematical modeling in non-equilibrium statistical physics, namely with equilibrium and kinetic Monte Carlo simulations, many-particle Brownian dynamics simulations, big data processing and analysis, basics of machine learning tools, and parallel cluster computing. - Mathematical and numerical methods as taught in the MSc programme "Matematické a počítačové modelování ve fyzice (FMPMP)". |
| Preliminary scope of work in English |
| Natural complex systems like neurons in the brain, bacteria colonies, groups of insects, and even flocks of birds, are known to exhibit long-range correlations. Their presence is frequently explained by means of to the concept of self-organized criticality and thus their theoretical investigations and modeling are usually focused on verification and classification of the emergent critical behavior. In this thesis, we shall focus on a different perspective. Our main assumption will be that individuals, which would like to develop a collective behavior within their group, must build long-ranged correlations, which would allow effective communication within the whole group. This assumption will determine the types of interactions between agents. We aim to test the hypothesis that optimization of correlations between a given set of agents as a function of their interactions can yield the observed group behavior. Results of this research will help to understand how interactions in groups of living agents arise and adapt to changing environments. They will be also useful in designing optimal control of groups of artificial robots. Interactions predicted for the system of active Brownian particles will be realized and tested experimentally in a real-world environment.
Required skills of the candidate: - Experience with and basic knowledge of mathematical modeling in non-equilibrium statistical physics, namely with equilibrium and kinetic Monte Carlo simulations, many-particle Brownian dynamics simulations, big data processing and analysis, basics of machine learning tools, and parallel cluster computing. - Mathematical and numerical methods as taught in the MSc programme "Matematické a počítačové modelování ve fyzice (FMPMP)". |