Learning protocols for the fast and efficient control of active matter.

ABSTRACT: Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. 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

SUBMITTER: Casert C 

PROVIDER: S-EPMC11500414 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature