Learning how to find targets in the micro-world: the case of intermittent active Brownian particles
Michele Caraglio, Harpreet Kaur, Lukas J. Fiderer, Andrea López-Incera, Hans J. Briegel, Thomas Franosch, Gorka Muñoz-Gil
Abstract
the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle's self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior.
Topics & Concepts
Brownian motionOddsStatistical physicsVersaPhysicsClassical mechanicsComputer scienceQuantum mechanicsMachine learningDatabaseLogistic regressionMicro and Nano RoboticsDiffusion and Search DynamicsMolecular Communication and Nanonetworks