Number fluctuation signals N(t) distinguish self-propelled particle models via differences in reorientation dynamics.
Ramaswamy, The mechanics and statistics of active matter, Annual Review of Condensed Matter Physics1, 323 (2010)
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Exact solution for anisotropic mobility in trapped self-propelled particles yields non-monotonic negative excess kurtosis and a strictly sub-Gaussian steady-state distribution that displaces the particle into high-potential regions.
Self-propulsion noise statistics define speed limits on non-equilibrium transitions in active matter, with non-stationary initials allowing faster cooling than passive protocols.
An effective reward function emerges that fully governs the evolution of policy distributions across the population, yielding closed equations for mean and variance under Gaussian assumptions.
Magnetic field on 3D active Brownian particles produces long-time drift plus enhanced diffusion, non-Gaussian statistics at intermediate times, and field-sensitive first-passage behavior.
A projection-operator perturbative framework yields an analytic transition rate for rare events in 1D active particles that is accurate across all persistence times via a rational approximation of the small- and large-persistence asymptotics.
An agent-based model with orientation-weighted velocity-dependent alignment generates disordered, flocking, jammed, and active-crystal-like collective phases by varying alignment strength.
citing papers explorer
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Number fluctuations distinguish different self-propelling dynamics
Number fluctuation signals N(t) distinguish self-propelled particle models via differences in reorientation dynamics.
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Mobility Anisotropy Reshapes Self-Propelled Motion
Exact solution for anisotropic mobility in trapped self-propelled particles yields non-monotonic negative excess kurtosis and a strictly sub-Gaussian steady-state distribution that displaces the particle into high-potential regions.
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Self-propulsion protocols for swift non-equilibrium state transitions and enhanced cooling in active systems
Self-propulsion noise statistics define speed limits on non-equilibrium transitions in active matter, with non-stationary initials allowing faster cooling than passive protocols.
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Theory of collective learning in populations of adaptive agents
An effective reward function emerges that fully governs the evolution of policy distributions across the population, yielding closed equations for mean and variance under Gaussian assumptions.
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Drift-diffusion interplay in active Brownian particles under orienting field
Magnetic field on 3D active Brownian particles produces long-time drift plus enhanced diffusion, non-Gaussian statistics at intermediate times, and field-sensitive first-passage behavior.
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General perturbative framework for kinetics of rare transitions in 1-dimensional active particle systems
A projection-operator perturbative framework yields an analytic transition rate for rare events in 1D active particles that is accurate across all persistence times via a rational approximation of the small- and large-persistence asymptotics.
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Collective dynamics of active matter with orientation-weighted alignment
An agent-based model with orientation-weighted velocity-dependent alignment generates disordered, flocking, jammed, and active-crystal-like collective phases by varying alignment strength.