Department of Physics and Centre for Neural Dynamics, University of Ottawa, Ottawa, Canada K1N 6N5.
Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, United Kingdom.
Phys Rev E. 2017 Jan;95(1-1):012114. doi: 10.1103/PhysRevE.95.012114. Epub 2017 Jan 9.
First-passage-time problems are ubiquitous across many fields of study, including transport processes in semiconductors and biological synapses, evolutionary game theory and percolation. Despite their prominence, first-passage-time calculations have proven to be particularly challenging. Analytical results to date have often been obtained under strong conditions, leaving most of the exploration of first-passage-time problems to direct numerical computations. Here we present an analytical approach that allows the derivation of first-passage-time distributions for the wide class of nondifferentiable Gaussian processes. We demonstrate that the concept of sign changes naturally generalizes the common practice of counting crossings to determine first-passage events. Our method works across a wide range of time-dependent boundaries and noise strengths, thus alleviating common hurdles in first-passage-time calculations.
首次通过时间问题在许多研究领域都普遍存在,包括半导体中的输运过程和生物突触、演化博弈论和渗流。尽管它们很突出,但首次通过时间的计算已被证明是特别具有挑战性的。迄今为止,分析结果通常是在强条件下得到的,这使得首次通过时间问题的大部分探索都依赖于直接的数值计算。在这里,我们提出了一种分析方法,该方法允许推导广泛的不可微高斯过程的首次通过时间分布。我们证明,符号变化的概念自然地推广了常见的通过计数交叉点来确定首次通过事件的做法。我们的方法适用于广泛的时变边界和噪声强度,因此缓解了首次通过时间计算中的常见障碍。