Quantifying information flow along a stochastic trajectory
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916009525379072 |
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| author | Oh, Yongjae Kwon, Euijoon Baek, Yongjoo |
| author_facet | Oh, Yongjae Kwon, Euijoon Baek, Yongjoo |
| contents | Stochastic information flow (SIF) quantifies information flow at the trajectory level, overcoming the limitations of conventional symmetric, ensemble-averaged measures. However, computational difficulties have hindered the empirical application of the SIF. In this work, we propose a scalable deep-learning method for estimating the SIF from general time-series data. Its applications to an exactly solvable two-particle model, Kuramoto oscillators, and empirical trajectories of interacting motile cells demonstrate the utility of SIF as a data-driven indicator of cooperative structures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_13509 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantifying information flow along a stochastic trajectory Oh, Yongjae Kwon, Euijoon Baek, Yongjoo Statistical Mechanics Stochastic information flow (SIF) quantifies information flow at the trajectory level, overcoming the limitations of conventional symmetric, ensemble-averaged measures. However, computational difficulties have hindered the empirical application of the SIF. In this work, we propose a scalable deep-learning method for estimating the SIF from general time-series data. Its applications to an exactly solvable two-particle model, Kuramoto oscillators, and empirical trajectories of interacting motile cells demonstrate the utility of SIF as a data-driven indicator of cooperative structures. |
| title | Quantifying information flow along a stochastic trajectory |
| topic | Statistical Mechanics |
| url | https://arxiv.org/abs/2605.13509 |