论文标题
马虎模型分析标识了分叉参数，而没有正常形式分析
Sloppy model analysis identifies bifurcation parameters without Normal Form analysis
论文作者
论文摘要
分叉现象在多维多参数动态系统中很常见。正常形式理论表明，分叉本身是由相对较少的参数驱动的。但是，这些通常是表达方程的裸参数的非线性组合。发现重新聚集以将这种复杂的原始方程转换为正常形式通常是非常困难的，并且在封闭形式中甚至可能不存在重新聚集化。最近的进步将信息的几何形状和分叉与重新归一化组联系在一起。在这里，我们表明草率的模型分析（一种信息几何方法）可以直接用于增加时间尺度的分叉，以迅速表征系统的拓扑不均匀性，无论系统是否处于正常形式。我们预计，我们称这种新颖的分析方法（我们称之为淘汰的信息几何形状（TWIG））将在应用网络分析中有用。
Bifurcation phenomena are common in multi-dimensional multi-parameter dynamical systems. Normal form theory suggests that the bifurcations themselves are driven by relatively few parameters; however, these are often nonlinear combinations of the bare parameters in which the equations are expressed. Discovering reparameterizations to transform such complex original equations into normal-form is often very difficult, and the reparameterization may not even exist in a closed-form. Recent advancements have tied both information geometry and bifurcations to the Renormalization Group. Here, we show that sloppy model analysis (a method of information geometry) can be used directly on bifurcations of increasing time scales to rapidly characterize the system's topological inhomogeneities, whether the system is in normal form or not. We anticipate that this novel analytical method, which we call time-widening information geometry (TWIG), will be useful in applied network analysis.
