Dan Shafir, Alessio Squarcini, Stanislav Burov, Thomas Franosch
{"title":"离散时间中的驱动洛伦兹模型","authors":"Dan Shafir, Alessio Squarcini, Stanislav Burov, Thomas Franosch","doi":"arxiv-2409.02696","DOIUrl":null,"url":null,"abstract":"We consider a tracer particle performing a random walk on a two-dimensional\nlattice in the presence of immobile hard obstacles. Starting from equilibrium,\na constant force pulling on the particle is switched on, driving the system to\na new stationary state. Our study calculates displacement moments in discrete\ntime (number of steps $N$) for an arbitrarily strong constant driving force,\nexact to first order in obstacle density. We find that for fixed driving force\n$F$, the approach to the terminal discrete velocity scales as $\\sim N^{-1}\n\\exp(- N F^2 / 16)$ for small $F$, differing significantly from the $\\sim\nN^{-1}$ prediction of linear response. Besides a non-analytic dependence on the\nforce and breakdown of Einstein's linear response, our results show that\nfluctuations in the directions of the force are enhanced in the presence of\nobstacles. Notably, the variance grows as $\\sim N^3$ (superdiffusion) for $F\n\\to \\infty$ at intermediate steps, reverting to normal diffusion ($\\sim N$) at\nlarger steps, a behavior previously observed in continuous time but\ndemonstrated here in discrete steps for the first time. Unlike the exponential\nwaiting time case, the superdiffusion regime starts immediately at $N=1$. The\nframework presented allows considering any type of waiting-time distribution\nbetween steps and transition to continuous time using subordination methods.\nOur findings are also validated through computer simulations.","PeriodicalId":501520,"journal":{"name":"arXiv - PHYS - Statistical Mechanics","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Driven Lorentz model in discrete time\",\"authors\":\"Dan Shafir, Alessio Squarcini, Stanislav Burov, Thomas Franosch\",\"doi\":\"arxiv-2409.02696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider a tracer particle performing a random walk on a two-dimensional\\nlattice in the presence of immobile hard obstacles. Starting from equilibrium,\\na constant force pulling on the particle is switched on, driving the system to\\na new stationary state. Our study calculates displacement moments in discrete\\ntime (number of steps $N$) for an arbitrarily strong constant driving force,\\nexact to first order in obstacle density. We find that for fixed driving force\\n$F$, the approach to the terminal discrete velocity scales as $\\\\sim N^{-1}\\n\\\\exp(- N F^2 / 16)$ for small $F$, differing significantly from the $\\\\sim\\nN^{-1}$ prediction of linear response. Besides a non-analytic dependence on the\\nforce and breakdown of Einstein's linear response, our results show that\\nfluctuations in the directions of the force are enhanced in the presence of\\nobstacles. Notably, the variance grows as $\\\\sim N^3$ (superdiffusion) for $F\\n\\\\to \\\\infty$ at intermediate steps, reverting to normal diffusion ($\\\\sim N$) at\\nlarger steps, a behavior previously observed in continuous time but\\ndemonstrated here in discrete steps for the first time. Unlike the exponential\\nwaiting time case, the superdiffusion regime starts immediately at $N=1$. The\\nframework presented allows considering any type of waiting-time distribution\\nbetween steps and transition to continuous time using subordination methods.\\nOur findings are also validated through computer simulations.\",\"PeriodicalId\":501520,\"journal\":{\"name\":\"arXiv - PHYS - Statistical Mechanics\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Statistical Mechanics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.02696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Statistical Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.02696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We consider a tracer particle performing a random walk on a two-dimensional
lattice in the presence of immobile hard obstacles. Starting from equilibrium,
a constant force pulling on the particle is switched on, driving the system to
a new stationary state. Our study calculates displacement moments in discrete
time (number of steps $N$) for an arbitrarily strong constant driving force,
exact to first order in obstacle density. We find that for fixed driving force
$F$, the approach to the terminal discrete velocity scales as $\sim N^{-1}
\exp(- N F^2 / 16)$ for small $F$, differing significantly from the $\sim
N^{-1}$ prediction of linear response. Besides a non-analytic dependence on the
force and breakdown of Einstein's linear response, our results show that
fluctuations in the directions of the force are enhanced in the presence of
obstacles. Notably, the variance grows as $\sim N^3$ (superdiffusion) for $F
\to \infty$ at intermediate steps, reverting to normal diffusion ($\sim N$) at
larger steps, a behavior previously observed in continuous time but
demonstrated here in discrete steps for the first time. Unlike the exponential
waiting time case, the superdiffusion regime starts immediately at $N=1$. The
framework presented allows considering any type of waiting-time distribution
between steps and transition to continuous time using subordination methods.
Our findings are also validated through computer simulations.