Search with stochastic home returns can expedite classical first passage under resetting

Classical first passage under resetting is a paradigm in the search process. Despite its multitude of applications across interdisciplinary sciences, experimental realizations of such resetting processes posit practical challenges in calibrating these zero time irreversible transitions. Here, we consider a strategy in which resetting is performed using finite-time return protocols in lieu of instantaneous returns. These controls could also be accompanied with random fluctuations or errors allowing target detection even during the return phase. To better understand the phenomena, we develop a unified renewal approach that can encapsulate arbitrary search processes centered around home in a fairly general topography containing targets, various resetting times, and return mechanisms in arbitrary dimensions. While such finite-time protocols would apparently seem to prolong the overall search time in comparison to the instantaneous resetting process, we show on the contrary that a significant speed-up can be gained by leveraging the stochasticity in home returns. The formalism is then explored to reveal a universal criterion distilling the benefits of this strategy. We demonstrate how this general principle can be utilized to improve overall performance of a one-dimensional diffusive search process reinforced with experimentally feasible parameters. We believe that such strategies designed with inherent randomness can be made optimal with precise controllability in complex search processes.

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Institutions

Publication Details

Journal
Physical review. E
Published
2024-10-07
DOI
https://doi.org/10.1103/physreve.110.l042101
Citations
8
Primary Topic
Diffusion and Search Dynamics
Type
article
Field-Weighted Citation Impact
1.87

Funders

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article

Search with stochastic home returns can expedite classical first passage under resetting

Anupam Kundu, A. Pal, Arup Biswas
8 citations
Physical review. E
Diffusion and Search Dynamics
1.87
article

Search with stochastic home returns can expedite classical first passage under resetting

Anupam Kundu, A. Pal, Arup Biswas
article en
8 citations

Abstract

Classical first passage under resetting is a paradigm in the search process. Despite its multitude of applications across interdisciplinary sciences, experimental realizations of such resetting processes posit practical challenges in calibrating these zero time irreversible transitions. Here, we consider a strategy in which resetting is performed using finite-time return protocols in lieu of instantaneous returns. These controls could also be accompanied with random fluctuations or errors allowing target detection even during the return phase. To better understand the phenomena, we develop a unified renewal approach that can encapsulate arbitrary search processes centered around home in a fairly general topography containing targets, various resetting times, and return mechanisms in arbitrary dimensions. While such finite-time protocols would apparently seem to prolong the overall search time in comparison to the instantaneous resetting process, we show on the contrary that a significant speed-up can be gained by leveraging the stochasticity in home returns. The formalism is then explored to reveal a universal criterion distilling the benefits of this strategy. We demonstrate how this general principle can be utilized to improve overall performance of a one-dimensional diffusive search process reinforced with experimentally feasible parameters. We believe that such strategies designed with inherent randomness can be made optimal with precise controllability in complex search processes.

Physical review. EVol. 110(4)
Homi Bhabha National Institute (IN), International Centre for Theoretical Sciences (IN), Institute of Mathematical Sciences (IN)
Department of Science and Technology, Ministry of Science and Technology, India, Department of Atomic Energy, Government of India, Science and Engineering Research Board
Decent work and economic growth
Openalex Percentile: Top 14%
Diffusion and Search Dynamics
1.87
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