临界跃迁与微扰增长方向
Critical Transitions and Perturbation Growth Directions
摘要：
临界跃迁发生在各种动力系统中。在这里，我们采用混沌的量词来识别复杂系统的动态结构的变化之前的关键过渡。作为合适的指标变量的关键转变，我们考虑增长率和方向的协变李雅普诺夫向量的变化。研究几种快慢系统模型中的临界转变，即，一个耦合FitzHugh-Nagumo振荡器网络，约瑟夫森结模型和Hindmarsh-Rose模型，我们发现协变李雅普诺夫向量之间的相切是一个共同的，可能是一般的关键转变过程中的特征。我们进一步证明，这种偏离双曲线动力学的关键转变的发生，通过使用它作为一个指标变量，并通过接收器操作特征曲线评估预测的成功。在存在噪声的情况下，我们发现协变李雅普诺夫向量的对齐和有限时间李雅普诺夫指数的变化在宣布临界转变方面比常见的指示变量更成功，例如，方差的有限时间估计此外，我们提出了一种新的方法估计近似的协变李雅普诺夫向量的未来轨迹的系统的知识。我们发现，这些近似的协变李雅普诺夫向量也可以应用于预测临界转变。
英文摘要：
Critical transitions occur in a variety of dynamical systems. Here, we employ quantifiers of chaos to identify changes in the dynamical structure of complex systems preceding critical transitions. As suitable indicator variables for critical transitions, we consider changes in growth rates and directions of covariant Lyapunov vectors. Studying critical transitions in several models of fast-slow systems, i.e., a network of coupled FitzHugh-Nagumo oscillators, models for Josephson junctions and the Hindmarsh-Rose model, we find that tangencies between covariant Lyapunov vectors are a common and maybe generic feature during critical transitions. We further demonstrate that this deviation from hyperbolic dynamics is linked to the occurrence of critical transitions by using it as an indicator variable and evaluating the prediction success through receiver operating characteristic curves. In the presence of noise, we find the alignment of covariant Lyapunov vectors and changes in finite-time Lyapunov exponents to be more successful in announcing critical transitions than common indicator variables as, e.g., finite-time estimates of the variance. Additionally, we propose a new method for estimating approximations of covariant Lyapunov vectors without knowledge of the future trajectory of the system. We find that these approximated covariant Lyapunov vectors can also be applied to predict critical transitions.
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