论文标题
蒙特卡洛物理机器：连续随机运输网络中模式形成的特征
Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks
论文作者
论文摘要
我们提出了蒙特卡洛（Monte）Carlo Physarum机器：一种计算模型，适用于从稀疏2D和3D数据中重建连续传输网络的计算模型。 MCPM是琼斯基于2010年代理的模型的概率概括，用于模拟Physarum Polysarum粘液模具的生长。我们将MCPM与琼斯在理论上进行比较，并描述了一种特定于任务的变体，旨在重建宇宙中气体和暗物质的大规模分布，称为宇宙网络。为了分析新模型，我们首先探讨了MCPM的自我模式行为，显示了该模型从几何直觉参数产生的广泛连续网络样形态（称为“多形”）。然后，将MCPM应用于模拟和观察性宇宙学数据集，然后评估其生成宇宙网络一致的3D密度图的能力。最后，我们研究了MCPM可能有用的其他可能任务，以及与域特异性数据拟合的几个示例作为概念证明。
We present Monte Carlo Physarum Machine: a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's 2010 agent-based model for simulating the growth of Physarum polycephalum slime mold. We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the Cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies -- called "polyphorms" -- that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological datasets, we then evaluate its ability to produce consistent 3D density maps of the Cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.
