Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session N00: Poster Session II (11:30am-2:30pm CST)
11:30 AM,
Wednesday, March 6, 2024
Room: Hall BC
Sponsoring
Unit:
APS/SPS
Abstract: N00.00201 : Signature of out-of-equilibrium dynamics in a partially observed system*
Presenter:
jean-baptiste masson
(institut pasteur - CNRS - Universite paris cite - INRIA)
Authors:
jean-baptiste masson
(institut pasteur - CNRS - Universite paris cite - INRIA)
christian L vestergaard
(institut pasteur - CNRS - Universite paris cite - INRIA)
alexander serov
(institut pasteur - CNRS - Universite paris cite - INRIA)
Collaboration:
Decision and Bayesian Computation, Institut Pasteur, UPC, CNRS UMR 3751,
Epimethee, INRIA,
There are numerous challenges in statistically testing whether recordings from a dynamical system show signatures of out-of-equilibrium dynamics. It is common to be able to monitor only some but not all relevant degrees of freedom of a dynamic system, which increases the difficulty of the already challenging inference task. For example, we can record the motion of a fluorescent particle in a cell but do not have direct access to the dynamics of structures with which it interacts. Inspired by the development of [Battle extit{et al.}, Science 352, 604--607 (2016)], we show that a statistical signature of out-of-equilibrium dynamics can be detected in a partially observed system. Focusing on the classic dumbbell with two coupled particles driven by Brownian noise at different temperatures, we show that a spectral Bayesian approach can reliably detect out-of-equilibrium dynamics from the trajectory of a single particle. We study the statistical properties of this inference and discuss future extensions.
*This study was funded by the Institut Pasteur, L’Agence Nationale de la Recherche (TRamWAy, ANR-17-CE23-0016 , the INCEPTION project (PIA/ANR-16-CONV-0005, OG), and the “Investissements d’avenir” programme under the management of Agence Nationale de la Recherche, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).
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