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In this paper, we study the problem of identifying the partial topology of complex dynamical networks via a pinning mechanism. By using the network synchronization theory and the adaptive feedback controlling method, we propose a method which can greatly reduce the number of nodes and observers in the response network. Particularly, this method can also identify the whole topology of complex networks. A theorem is established rigorously, from which some corollaries are also derived in order to make our method more cost-effective. Several numerical examples are provided to verify the effectiveness of the proposed method. In the simulation, an approach is also given to avoid possible identification failure caused by inner synchronization of the drive network. [ABSTRACT FROM AUTHOR]

Recently, a rigorous formalism has been established for information flow and causality within dynamical systems with respect to Shannon entropy. In this study, we re-establish the formalism with respect to relative entropy, or Kullback-Leiber divergence, a well-accepted measure of predictability because of its appealing properties such as invariance upon nonlinear transformation and consistency with the second law of thermodynamics. Different from previous studies (which yield consistent results only for 2D systems), the resulting information flow, say T , is precisely the same as that with respect to Shannon entropy for systems of arbitrary dimensionality, except for a minus sign (reflecting the opposite notion of predictability vs. uncertainty). As before, T possesses a property called principle of nil causality, a fact that classical formalisms fail to verify in many situation. Besides, it proves to be invariant upon nonlinear transformation, indicating that the so-obtained information flow should be an intrinsic physical property. This formalism has been validated with the stochastic gradient system, a nonlinear system that admits an analytical equilibrium solution of the Boltzmann type. [ABSTRACT FROM AUTHOR]

Big data have become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems, where first principles modeling may be intractable. However, in many engineering systems, abrupt changes must be rapidly characterized based on limited, incomplete, and noisy data. Many leading automated learning techniques rely on unrealistically large data sets, and it is unclear how to leverage prior knowledge effectively to re-identify a model after an abrupt change. In this work, we propose a conceptual framework to recover parsimonious models of a system in response to abrupt changes in the low-data limit. First, the abrupt change is detected by comparing the estimated Lyapunov time of the data with the model prediction. Next, we apply the sparse identification of nonlinear dynamics (SINDy) regression to update a previously identified model with the fewest changes, either by addition, deletion, or modification of existing model terms. We demonstrate this sparse model recovery on several examples for abrupt system change detection in periodic and chaotic dynamical systems. Our examples show that sparse updates to a previously identified model perform better with less data, have lower runtime complexity, and are less sensitive to noise than identifying an entirely new model. The proposed abrupt-SINDy architecture provides a new paradigm for the rapid and efficient recovery of a system model after abrupt changes. [ABSTRACT FROM AUTHOR]

We prove two general theorems that determine the Lie and the Noether point symmetries for the equations of motion of a dynamical system thatmoves in a general Riemannian space under the action of a time dependent potential W(t, x) =ω(t)V(x). We apply the theorems to the case of a time dependent central potential and the harmonic oscillator and determine all Lie and Noether point symmetries. Finally we prove that these theorems also apply to the case of a dynamical system with linear dumping and study two examples. [ABSTRACT FROM AUTHOR]

The link between the Fisher information and Rényi entropies is explored. The relationship is based on a thermodynamical formalism based on Fisher information with a parameter, β, which is interpreted as the inverse temperature. The Fisher heat capacity is defined and found to be sensitive to changes of higher order than the analogous quantity in the conventional formulation. [ABSTRACT FROM AUTHOR]

We consider a network of identical pulse-coupled oscillators with delay and all-to-all coupling. We demonstrate that the discontinuous nature of the dynamics induces the appearance of isochronous regions--subsets of the phase space filled with periodic orbits having the same period. For each fixed value of the network parameters, such an isochronous region corresponds to a subset of initial states on an appropriate surface of section with non-zero dimensions such that all periodic orbits in this set have qualitatively similar dynamical behaviour. We analytically and numerically study in detail such an isochronous region, give proof of its existence, and describe its properties. We further describe other isochronous regions that appear in the system. [ABSTRACT FROM AUTHOR]