Quantum active matter shows mean-squared displacement scaling as t^6 or t^7 derived analytically from a Wigner phase-space master equation.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 1polarities
background 1representative citing papers
Candidate-dependent extremal alignment in topological active matter generates self-confined, spatially structured flocks by factorizing decision utility into average score times neighbor count.
Number fluctuation signals N(t) distinguish self-propelled particle models via differences in reorientation dynamics.
Inertial active chains show multiple MSD crossovers and systematically evolving non-Gaussian velocity distributions captured by excess kurtosis.
An agent-based model with orientation-weighted velocity-dependent alignment generates disordered, flocking, jammed, and active-crystal-like collective phases by varying alignment strength.
Machine learning on simulation observables produces a phase diagram for the Vicsek model that identifies a narrow coexistence region between ordered and disordered states.
citing papers explorer
-
Anomalous Mean-Squared Displacement in Quantum Active Matter from a Wigner Phase-Space Framework
Quantum active matter shows mean-squared displacement scaling as t^6 or t^7 derived analytically from a Wigner phase-space master equation.
-
Spatially Structured Cohesion from Extremal Alignment in Topological Active Matter
Candidate-dependent extremal alignment in topological active matter generates self-confined, spatially structured flocks by factorizing decision utility into average score times neighbor count.
-
Number fluctuations distinguish different self-propelling dynamics
Number fluctuation signals N(t) distinguish self-propelled particle models via differences in reorientation dynamics.
-
Crossover dynamics and non-Gaussian fluctuations in inertial active chains
Inertial active chains show multiple MSD crossovers and systematically evolving non-Gaussian velocity distributions captured by excess kurtosis.
-
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.
-
Mapping the Phase Diagram of the Vicsek Model with Machine Learning
Machine learning on simulation observables produces a phase diagram for the Vicsek model that identifies a narrow coexistence region between ordered and disordered states.