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Bistable systems with stochastic noise: Virtues and limits of effective one-dimensional Langevin equations

机译:具有随机噪声的双稳态系统:有效一维Langevin方程的优点和极限

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The understanding of the statistical properties and of the dynamics of multistable systems is gaining more and more importance in a vast variety of scientific fields. This is especially relevant for the investigation of the tipping points of complex systems. Sometimes, in order to understand the time series of given observables exhibiting bimodal distributions, simple one-dimensional Langevin models are fitted to reproduce the observed statistical properties, and used to investing-ate the projected dynamics of the observable. This is of great relevance for studying potential catastrophic changes in the properties of the underlying system or resonant behaviours like those related to stochastic resonance-like mechanisms. In this paper, we propose a framework for encasing this kind of studies, using simple box models of the oceanic circulation and choosing as observable the strength of the thermohaline circulation. We study the statistical properties of the transitions between the two modes of operation of the thermohaline circulation under symmetric boundary forcings and test their agreement with simplified one-dimensional phenomenological theories. We extend our analysis to include stochastic resonance-like amplification processes. We conclude that fitted one-dimensional Langevin models, when closely scrutinised, may result to be more ad-hoc than they seem, lacking robustness and/or well-posedness. They should be treated with care, more as an empiric descriptive tool than as methodology with predictive power.
机译:在各种各样的科学领域中,对统计特性和多稳态系统动力学的理解越来越重要。这对于研究复杂系统的临界点特别重要。有时,为了了解显示双峰分布的给定可观察物的时间序列,拟合了简单的一维Langevin模型以重现观察到的统计属性,并用于估计可观察物的预计动力学。这对于研究基础系统的潜在潜在灾难性变化或共振行为(如与类似随机共振的机制相关的共振行为)具有重大意义。在本文中,我们提出了一个框架,用于进行此类研究,使用简单的海洋环流箱模型并选择可观测的热盐环流强度。我们研究了对称边界强迫作用下热盐环流两种运行模式之间转变的统计特性,并用简化的一维现象学理论检验了它们的一致性。我们扩展了我们的分析,以包括类似随机共振的扩增过程。我们得出的结论是,如果仔细研究拟合的一维Langevin模型,结果可能会比它们看起来更特别,缺乏鲁棒性和/或适定性。应该谨慎对待它们,更多地将其视为经验描述工具,而不是具有预测能力的方法。

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