Thomas Peter J, Lindner Benjamin
Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, Haus 2, 10115 Berlin, Germany and Department of Physics, Humboldt University Berlin, Newtonstraße 15, 12489 Berlin, Germany.
Phys Rev E. 2019 Jun;99(6-1):062221. doi: 10.1103/PhysRevE.99.062221.
Stochastic oscillators play a prominent role in different fields of science. Their simplified description in terms of a phase has been advocated by different authors using distinct phase definitions in the stochastic case. One notion of phase that we put forward previously, the asymptotic phase of a stochastic oscillator, is based on the eigenfunction expansion of its probability density. More specifically, it is given by the complex argument of the eigenfunction of the backward operator corresponding to the least-negative eigenvalue. Formally, besides the "backward" phase, one can also define the "forward" phase as the complex argument of the eigenfunction of the forward Kolomogorov operator corresponding to the least-negative eigenvalue. Until now, the intuition about these phase descriptions has been limited. Here we study these definitions for a process that is analytically tractable, the two-dimensional Ornstein-Uhlenbeck process with complex eigenvalues. For this process, (i) we give explicit expressions for the two phases; (ii) we demonstrate that the isochrons are always the spokes of a wheel but that (iii) the spacing of these isochrons (their angular density) is different for backward and forward phases; (iv) we show that the isochrons of the backward phase are completely determined by the deterministic part of the vector field, whereas the forward phase also depends on the noise matrix; and (v) we demonstrate that the mean progression of the backward phase in time is always uniform, whereas this is not true for the forward phase except in the rotationally symmetric case. We illustrate our analytical results for a number of qualitatively different cases.
随机振荡器在不同科学领域中发挥着重要作用。不同作者根据随机情况下不同的相位定义,主张用相位对其进行简化描述。我们之前提出的一种相位概念,即随机振荡器的渐近相位,是基于其概率密度的本征函数展开。更具体地说,它由对应于最小负特征值的后向算子的本征函数的复幅角给出。形式上,除了“后向”相位,还可以将“前向”相位定义为对应于最小负特征值的前向柯尔莫哥洛夫算子的本征函数的复幅角。到目前为止,对这些相位描述的直观理解一直很有限。在这里,我们针对一个易于解析处理的过程——具有复特征值的二维奥恩斯坦 - 乌伦贝克过程,研究这些定义。对于这个过程,(i)我们给出了两个相位的显式表达式;(ii)我们证明等时线总是呈轮辐状,但(iii)后向和前向相位的这些等时线间距(它们的角密度)不同;(iv)我们表明后向相位的等时线完全由向量场的确定性部分决定,而前向相位还取决于噪声矩阵;并且(v)我们证明后向相位在时间上的平均进展总是均匀的,而前向相位并非如此,除非在旋转对称的情况下。我们针对一些定性不同的情况说明了我们的分析结果。