标题： 提升的TASEP中的速度俘获及真自避随机游走 显示英文标题

标题： Velocity trapping in the lifted TASEP and the true self-avoiding random walk

摘要： 我们讨论了非可逆马尔可夫链蒙特卡罗算法，对于粒子系统而言，这些算法能够严格采样位置玻尔兹曼分布，并且具有比物理动力学更快的动力学速度。 这些算法均具有非热速度分布的特点。 它们以提升的TASEP（完全非对称简单排斥过程）为例，该过程是一维晶格上的事件链蒙特卡罗方法的简化形式。 我们通过局部密度与粒子速度之间的相关性所引起的“速度陷阱”来分析其动力学特性。 这使我们能够对其非平衡混合时间尺度提出一个猜想，并合理化其平衡超扩散时间尺度。 这两个尺度都快于（未提升的）TASEP。 通过对提升的TASEP进行多粒子真实自回避随机游走的许多实现的分析，进一步验证了这两个尺度。 我们还讨论了一维晶格模型之外以及超过一个物理维度的情况下的速度陷阱。 指出了超出物理学领域可能的应用。 显示英文摘要

摘要： We discuss non-reversible Markov-chain Monte Carlo algorithms that, for particle systems, rigorously sample the positional Boltzmann distribution and that have faster than physical dynamics. These algorithms all feature a non-thermal velocity distribution. They are exemplified by the lifted TASEP (totally asymmetric simple exclusion process), a one-dimensional lattice reduction of event-chain Monte Carlo. We analyze its dynamics in terms of a velocity trapping that arises from correlations between the local density and the particle velocities. This allows us to formulate a conjecture for its out-of-equilibrium mixing time scale, and to rationalize its equilibrium superdiffusive time scale. Both scales are faster than for the (unlifted) TASEP. They are further justified by our analysis of the lifted TASEP in terms of many-particle realizations of true self-avoiding random walks. We discuss velocity trapping beyond the case of one-dimensional lattice models and in more than one physical dimensions. Possible applications beyond physics are pointed out.