论文标题
步长是连续细胞自动机中的结果参数
Step Size is a Consequential Parameter in Continuous Cellular Automata
论文作者
论文摘要
连续细胞自动机（CA）中的步长在自组织模式的稳定性和行为中起重要作用。连续的CA动力学由公式与基于物理基于物理的普通微分方程的数值估计，特别是Euler的方法非常相似，该方法通常不准确且不稳定。连续ca无渐近地接近更准确的CA动力学估计值，而是可以支持不同步骤大小的不同范围的不同自组织模式。我们讨论了几个移动模式的示例，这些示例在台阶尺寸上变得不稳定，这些示例太小且太大。此外，单个移动模式可能在各个步骤尺寸的范围内表现出质量不同的行为。我们演示了在Lenia框架中实现的连续CA及其变体Glaberish中阶梯大小在模式稳定性和定性行为中的影响的示例。
Step size in continuous cellular automata (CA) plays an important role in the stability and behavior of self-organizing patterns. Continous CA dynamics are defined by formula very similar to numerical estimation of physics-based ordinary differential equations, specifically Euler's method, for which a large step size is often inaccurate and unstable. Rather than asymptotically approaching more accurate estimates of CA dynamics with decreasing step size, continuous CA may support different self-organizing patterns at different ranges of step size. We discuss several examples of mobile patterns that become unstable at step sizes that are too small as well as too large. Additionally, an individual mobile pattern may exhibit qualitatively different behavior across a range of step sizes. We demonstrate examples of the effects of step size in pattern stability and qualitative behavior in continuous CA implemented in the Lenia framework and its variant, Glaberish.
