Ben Fulcher
@bendfulcher.bsky.social
I lead the Dynamics and Neural Systems Group at the School of Physics, the University of Sydney.
We develop time series tools & physical models to understand the dynamics of complex (usually neural) systems.
Also: @[email protected]
We develop time series tools & physical models to understand the dynamics of complex (usually neural) systems.
Also: @[email protected]
If you're interested in the statistics of time-irreversibility, and how they could be used to capture novel, interpretable properties from real-world time series, take a look:
arxiv.org/abs/2511.15991
(Code here: github.com/DynamicsAndN...)
arxiv.org/abs/2511.15991
(Code here: github.com/DynamicsAndN...)
Identifying statistical indicators of temporal asymmetry using a data-driven approach
The dynamics of time-reversible systems are statistically indistinguishable when observed forward or backward in time. A rich literature of statistical methods to distinguish irreversible dynamics fro...
arxiv.org
November 21, 2025 at 3:26 AM
If you're interested in the statistics of time-irreversibility, and how they could be used to capture novel, interpretable properties from real-world time series, take a look:
arxiv.org/abs/2511.15991
(Code here: github.com/DynamicsAndN...)
arxiv.org/abs/2511.15991
(Code here: github.com/DynamicsAndN...)
The breath of comparison (of both methods and processes) also allowed us to demonstrate that all tested indices of irreversibility had weaknesses: i.e., we could always find an irreversibile process on which any given irreverisbility index will fail to detect irreversibility.
November 21, 2025 at 3:26 AM
The breath of comparison (of both methods and processes) also allowed us to demonstrate that all tested indices of irreversibility had weaknesses: i.e., we could always find an irreversibile process on which any given irreverisbility index will fail to detect irreversibility.
We found key families of algorithmic constructions that were could accurately index irreversibility: (i) generalized autocorrelation functions; (ii) symbolic sequences; and (iii) forecasting-derived metrics. Some recapitulate concepts studied previously but in isolation; others are novel directions
November 21, 2025 at 3:26 AM
We found key families of algorithmic constructions that were could accurately index irreversibility: (i) generalized autocorrelation functions; (ii) symbolic sequences; and (iii) forecasting-derived metrics. Some recapitulate concepts studied previously but in isolation; others are novel directions
He we compared >6000 time-series metrics to index time reversibility from simulations of 35 different reversible and irreversible processes
November 21, 2025 at 3:26 AM
He we compared >6000 time-series metrics to index time reversibility from simulations of 35 different reversible and irreversible processes
Quantifying time reversibility from data is important because it connects to concepts in thermodynamics (entropy production of non-equilibrium systems) & constrains the system's generative mechanisms (by ruling out linear dynamics; cf. related concepts of non-Gaussianity & nonlinearity)
November 21, 2025 at 3:26 AM
Quantifying time reversibility from data is important because it connects to concepts in thermodynamics (entropy production of non-equilibrium systems) & constrains the system's generative mechanisms (by ruling out linear dynamics; cf. related concepts of non-Gaussianity & nonlinearity)