Learning protocols for the fast and efficient control of active matter
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910387890290688 |
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| author | Casert, Corneel Whitelam, Stephen |
| author_facet | Casert, Corneel Whitelam, Stephen |
| contents | We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in the form of a neural network we use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18823 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning protocols for the fast and efficient control of active matter Casert, Corneel Whitelam, Stephen Statistical Mechanics Disordered Systems and Neural Networks Soft Condensed Matter We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in the form of a neural network we use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory. |
| title | Learning protocols for the fast and efficient control of active matter |
| topic | Statistical Mechanics Disordered Systems and Neural Networks Soft Condensed Matter |
| url | https://arxiv.org/abs/2402.18823 |