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Warning: Cannot modify header information - headers already sent by (output started at /tmp/nav.phptjy9xU:2) in /tmp/nav.phptjy9xU on line 7 Seminare der Theoretischen Physik
Active Brownian particles and aligning Vicsek particles are the most frequently employed models to describe the dynamics and collective behavior of motile active matter [1,2]. However, for living systems, activity and locomotion is combined with sensing of the environment and adaption of motion. We study such system by considering “intelligent” active particles with visual perception
and self-steering in dry [3-6] and wet systems [7,8]. Our model of cognitive self-steering particles consists of intelligent active Brownian particles
(iABPs), which are equipped with an orientational response to an instantaneous visual input of the positions of neighbors within a vision cone -- with limited maneuverability (ability to change the propulsion direction) [3-5]. The redirection of particle motion can be either to follow other particles, or to avoid them. This model can be employed to study the collective motion of many
identical iAPBs [3,4], e.g. to describe the motion of pedestrians in confinement or at an intersection [5], but also the pursuit of a moving target by single predator [6]. In many systems, like flocks of birds and schools of fish, it is obvious that alignment of motion with neighbors is also important. The ratio of vision-related maneuverability and alignment-induced steering then controls swarm shapes and dynamics [4]. In wet systems, a combination of alignment interactions and hydrodynamic interactions plays an essential role [7,8], where particles are modelled as “intelligent” squirmer (iSquirmers). Here,pushers and pullers display very different emergent behaviors.
References:
[1] J. Elgeti, R.G. Winkler, G. Gompper, Rev. Prog. Phys. 78, 056601 (2015)
[2] G. Gompper et al., J. Phys. Condens. Matter 37, 143501 (2025).
[3] R.S. Negi, R.G. Winkler, G. Gompper, Soft Matter 18, 6167 (2022)
[4] R.S. Negi, R.G. Winkler, G. Gompper, Phys. Rev. Research 6, 013118 (2024)
[5] P. Iyer, R.S. Negi, A. Schadschneider, and G. Gompper, Commun. Phys. 7, 379 (2024)
[6] S. Goh, R.G. Winkler, G. Gompper, New J. Phys. 24, 093039 (2022)
[7] S. Goh, R.G. Winkler, G. Gompper, Commun. Phys. 6, 31 (2023)
[8] S. Goh, E. Westphal, R.G. Winkler, G. Gompper, Phys. Rev. Research 7, 013142 (2025)
I will review the information lattice and its use for characterizing many-body states and many-body dynamics. The information lattice provides a local decomposition of information (entanglement) into scales that allows us to characterize many-body states according to their distribution of information. Furthermore, under unitary time evolution the local information flows like a conserved fluid on the information lattice. In thermalizing systems the flow of information is from small scales to large scales. I will also discuss an approximate algorithm for time evolution that uses this fact to discard large scale inaccessible information in a way that conserves expectation values of local observables and allows us to reach large times for large system sizes, allowing the extraction of diffusion constants from the many-body dynamics.
