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Collaborative Research Center 910Optimization in Active Matter (19.06.2020)

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Optimization in active matter

Friday, 19th June 2020

Location: Technische Universität Berlin

Online via zoom

Straße des 17. Juni 135, 10623 Berlin

Guests are welcome!

 

 

Programme

Friday, 19th June 2020

 

Programme
15:00
Optimal navigation of self-propelled particles and microswimmers
Benno Liebchen, Technische Universität Darmstadt, Germany

16:00
Coffee Break
16:10
Learned navigation of smart active particles
Holger Stark, Technische Universität Berlin, Germany
 

16:35
Search efficiency of fractional Brownian motion in a random distribution of targets
Mohsen Khadem, Technische Universität Berlin, Germany
 
17:00
Discussion (open end)

Abstracts

Optimal navigation of self-propelled particles and microswimmers

Benno Liebchen, Technische Universität Darmstadt, Germany

The quest for the optimal navigation strategy in a complex environment is at the heart of microswimmer applications like cargo carriage or drug targeting to cancer cells. In this talk we formulate a variational Fermat's principle which determines the optimal path allowing a self-propelled active particle (which can freely steer but moves with a preferred speed) to reach a target fastest. For piecewise constant force or flow fields the principle leads to Snell's law from geometrical optics, showing that the optimal path to the target is piecewise linear, as for light rays, but with a generalized refraction law. For complex environments, like general 1D-, shear- or vortex-fields, we obtain exact analytical expressions for the optimal path, showing, for example, that microswimmers sometimes have to temporarily navigate away from their target to reach it fastest.

In the second half of this talk, we focus on microswimmers, which in contrast to dry active particles, create a characteristic flow field. This flow field induces hydrodynamic interactions with remote walls or obstacles which oblige the swimmers to take significant detours to reach their target fastest, even in the absence of external fields. Such strategic detours are particularly useful in the presence of fluctuations: they effectively protect microswimmers against fluctuations and allow them to reach a target (e.g. a food source) up to twice faster than when greedily heading straight towards it.




Learned navigation of smart active particles


Holger Stark, Technische Universität Berlin, Germany

Biological microswimmers move or need to navigate in complex environments such as porous soil or landscapes of external cues. For all the artificially generated microswimmers it is a challenge to mimic navigation strategies from biology. The talk addresses recent work where we look at optimal steering of active particles along the shortest path in a potential landscape and show how a smart active particle learns optimal steering via a reinforcement-learning strategy [1]. I also shortly introduce capillary condensation of a collection of active Brownian particles in a slit pore of varying width, which serves as a model pore [2].

[1] E. Schneider and H. Stark, EPL 127, 64003 (2019).
[2] M. Knežević and H. Stark, EPL 128, 40008 (2020).




Search efficiency of fractional Brownian motion in a random distribution of targets

Mohsen Khadem, Technische Universität Berlin, Germany


Efficiency of search for randomly distributed targets is a prominent problem in many branches of the sciences. For basic classes of stochastic processes such as Levy walks there are hypotheses around suggesting universal optimal efficiencies under variation of search intrinsic and extrinsic environmental parameters. Here we study fractional Brownian motion as a search process, which under parameter variation covers all diffusive regimes from sub- over normal to superdiffusion. Computer simulations of this search process in a random distribution of targets show that maximising search efficiencies very sensibly depends on the variation of both intrinsic and extrinsic parameters, the type of search, the type of targets and the choice of boundary conditions. Some of our results are explained by a simple analytical model.​

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