http://www.cnr.it/ontology/cnr/individuo/prodotto/ID288114
Dynamical maximum entropy approach to flocking (Articolo in rivista)
- Type
- Label
- Dynamical maximum entropy approach to flocking (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1103/PhysRevE.89.042707 (literal)
- Alternative label
Andrea Cavagna (1,2,3); Irene Giardina (1,2,3); Francesco Ginelli (4); Thierry Mora (5); Duccio Piovani (2); Raffaele Tavarone (2); Aleksandra M. Walczak (6) (2014)
Dynamical maximum entropy approach to flocking
in Physical review. E, Statistical, nonlinear, and soft matter physics (Print)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Andrea Cavagna (1,2,3); Irene Giardina (1,2,3); Francesco Ginelli (4); Thierry Mora (5); Duccio Piovani (2); Raffaele Tavarone (2); Aleksandra M. Walczak (6) (literal)
- Pagina inizio
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- Published 16 April 2014. (literal)
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- http://journals.aps.org/pre/abstract/10.1103/PhysRevE.89.042707 (literal)
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- ISI Web of Science (WOS) (literal)
- Scopu (literal)
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- (1) Istituto Sistemi Complessi, Consiglio Nazionale Delle Ricerche, UOS Sapienza, Rome, Italy
(2) Dipartimento di Fisica, Università Sapienza, Rome, Italy
(3) Initiative for the Theoretical Sciences, Graduate Center, City University of New York, NY, United States
(4) Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Aberdeen, United Kingdom
(5) Laboratoire de Physique Statistique, CNRS, École Normale Supérieure, Paris, France
(6) Laboratoire de Physique Théorique, CNRS, École Normale Supérieure, Paris, France (literal)
- Titolo
- Dynamical maximum entropy approach to flocking (literal)
- Abstract
- We derive a new method to infer from data the out-of-equilibrium alignment dynamics of collectively moving animal groups, by considering the maximum entropy model distribution consistent with temporal and spatial correlations of flight direction. When bird neighborhoods evolve rapidly, this dynamical inference correctly learns the parameters of the model, while a static one relying only on the spatial correlations fails. When neighbors change slowly and the detailed balance is satisfied, we recover the static procedure. We demonstrate the validity of the method on simulated data. The approach is applicable to other systems of active matter. (literal)
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