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arXiv 2211.08166nlin.CGcond-mat.stat-mechphysics.comp-ph
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基于统计力学的元胞自动机分类

Classification of Cellular Automata based on Statistical Mechanics

Luca Bertolani, Andrea Idini

AI总结:

本研究利用统计力学和热力学方法分析外总istic二维元胞自动机,通过三种途径计算热力学变量与势,判断规则是否对应理想气体及平衡态,提出一种稳健且具预测性的分类方法。

摘要:

元胞自动机是一组离散空间中的计算模型,其离散时间演化由邻域规则定义。它们被广泛用于模拟物理学及一般科学中的许多复杂系统。在本工作中,统计力学和热力学被用于分析一大类外总istic二维元胞自动机。根据三种不同的方法推导并计算了热力学变量和势,以确定某个元胞自动机规则所代表的系统是否类似于理想气体,以及该系统是否处于热力学平衡态。研究表明,这种分类对于特定规则集合而言足够稳健,并能预测其有趣的性质。

英文摘要:

Cellular automata are a set of computational models in discrete space that have a discrete time evolution defined by neighbourhood rules. They are used to simulate many complex systems in physics and science in general. In this work, statistical mechanics and thermodynamics are used to analyse a large set of outer totalistic two-dimensional cellular automata. Thermodynamic variables and potentials are derived and computed according to three different approaches to determine if a cellular automaton rule is representing a system akin to the ideal gas, in or out of the thermodynamical equilibrium. It is suggested that this classification is sufficiently robust and predictive of interesting properties for particular set of rules.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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