Dynamical maximum entropy approach to flocking.
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.
Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy and Dipartimento di Fisica, Università Sapienza, Rome, Italy and Initiative for the Theoretical Sciences, The Graduate Center, The City University of New York, New York, USA.
Andrea
A
Cavagna
Istituto Sistemi Complessi, Consiglio Nazionale delle Ricerche, UOS Sapienza, Rome, Italy and Dipartimento di Fisica, Università Sapienza, Rome, Italy and Initiative for the Theoretical Sciences, The Graduate Center, The City University of New York, New York, USA.
Irene
I
Giardina
SUPA, Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Aberdeen, UK.
Francesco
F
Ginelli
Laboratoire de physique statistique, CNRS, UPMC and École normale supérieure, Paris, France.
Thierry
T
Mora
Dipartimento di Fisica, Università Sapienza, Rome, Italy.
Duccio
D
Piovani
Dipartimento di Fisica, Università Sapienza, Rome, Italy.
Raffaele
R
Tavarone
Laboratoire de physique théorique, CNRS, UPMC and École normale supérieure, Paris, France.
Aleksandra M
AM
Walczak