TY - JOUR
T1 - Detecting hidden states in stochastic dynamical systems
AU - Succar, Rayan
AU - Boldini, Alain
AU - Porfiri, Maurizio
N1 - Publisher Copyright:
© 2024 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
PY - 2024/1
Y1 - 2024/1
N2 - Inferring the number of states of a stochastic system from partial measurements is a fundamental problem in physics, for which methodological tools remain scarce. It is sometimes difficult to distinguish the stochastic dynamical states from measurements, deceiving us into incorrect models and flawed understanding of natural phenomena. Here, we propose a model-free statistical framework, grounded in network and control theory, to estimate the number of states of a stochastic system from perceptible dynamics. The framework extends previous techniques for deterministic systems, based on the rank of ancillary matrices. We show applications of our approach to a variety of physics domains, such as statistical mechanics, biophysics, physical chemistry, and epidemiology.
AB - Inferring the number of states of a stochastic system from partial measurements is a fundamental problem in physics, for which methodological tools remain scarce. It is sometimes difficult to distinguish the stochastic dynamical states from measurements, deceiving us into incorrect models and flawed understanding of natural phenomena. Here, we propose a model-free statistical framework, grounded in network and control theory, to estimate the number of states of a stochastic system from perceptible dynamics. The framework extends previous techniques for deterministic systems, based on the rank of ancillary matrices. We show applications of our approach to a variety of physics domains, such as statistical mechanics, biophysics, physical chemistry, and epidemiology.
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U2 - 10.1103/PhysRevResearch.6.013149
DO - 10.1103/PhysRevResearch.6.013149
M3 - Article
AN - SCOPUS:85184853972
SN - 2643-1564
VL - 6
JO - Physical Review Research
JF - Physical Review Research
IS - 1
M1 - 013149
ER -