Publisher: AIP   (Total: 27 journals)   [Sort alphabetically]

Showing 1 - 27 of 27 Journals sorted by number of followers
Physics Today     Hybrid Journal   (Followers: 78, SJR: 0.66, CiteScore: 1)
J. of Applied Physics     Hybrid Journal   (Followers: 69, SJR: 0.739, CiteScore: 2)
American J. of Physics     Full-text available via subscription   (Followers: 58, SJR: 0.456, CiteScore: 1)
Physics of Fluids     Hybrid Journal   (Followers: 46, SJR: 1.19, CiteScore: 3)
Applied Physics Letters     Hybrid Journal   (Followers: 44, SJR: 1.382, CiteScore: 3)
J. of Chemical Physics     Hybrid Journal   (Followers: 36, SJR: 1.252, CiteScore: 2)
J. of Mathematical Physics     Hybrid Journal   (Followers: 25, SJR: 0.644, CiteScore: 1)
Review of Scientific Instruments     Hybrid Journal   (Followers: 20, SJR: 0.585, CiteScore: 1)
J. of Laser Applications     Full-text available via subscription   (Followers: 14, SJR: 0.741, CiteScore: 2)
APL Materials     Open Access   (Followers: 12, SJR: 1.63, CiteScore: 4)
J. of Renewable and Sustainable Energy     Hybrid Journal   (Followers: 11, SJR: 0.44, CiteScore: 1)
Applied Physics Reviews     Hybrid Journal   (Followers: 11, SJR: 4.156, CiteScore: 12)
Physics of Plasmas     Hybrid Journal   (Followers: 10, SJR: 0.576, CiteScore: 1)
Acoustics Today     Hybrid Journal   (Followers: 9)
Biomicrofluidics     Open Access   (Followers: 7, SJR: 0.592, CiteScore: 2)
AIP Advances     Open Access   (Followers: 7, SJR: 0.472, CiteScore: 1)
Low Temperature Physics     Hybrid Journal   (Followers: 6, SJR: 0.264, CiteScore: 1)
Structural Dynamics     Open Access   (Followers: 6, SJR: 1.625, CiteScore: 4)
J. of Physical and Chemical Reference Data     Hybrid Journal   (Followers: 4, SJR: 1.046, CiteScore: 3)
Chaos : An Interdisciplinary J. of Nonlinear Science     Hybrid Journal   (Followers: 3, SJR: 0.716, CiteScore: 2)
AIP Conference Proceedings     Full-text available via subscription   (Followers: 2)
Biointerphases     Open Access   (Followers: 1, SJR: 0.558, CiteScore: 2)
Chinese J. of Chemical Physics     Hybrid Journal   (Followers: 1, SJR: 0.24, CiteScore: 1)
Surface Science Spectra     Hybrid Journal   (Followers: 1, SJR: 0.416, CiteScore: 1)
Scilight     Full-text available via subscription  
APL Bioengineering     Open Access  
APL Photonics     Open Access  
Similar Journals
Journal Cover
Chaos : An Interdisciplinary Journal of Nonlinear Science
Journal Prestige (SJR): 0.716
Citation Impact (citeScore): 2
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1054-1500 - ISSN (Online) 1089-7682
Published by AIP Homepage  [27 journals]
  • Complex network growth model: Possible isomorphism between nonextensive
           statistical mechanics and random geometry

    • Free pre-print version: Loading...

      Authors: Constantino Tsallis, Rute Oliveira
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      In the realm of Boltzmann–Gibbs statistical mechanics, there are three well known isomorphic connections with random geometry, namely, (i) the Kasteleyn–Fortuin theorem, which connects the [math] limit of the [math]-state Potts ferromagnet with bond percolation, (ii) the isomorphism, which connects the [math] limit of the [math]-state Potts ferromagnet with random resistor networks, and (iii) the de Gennes isomorphism, which connects the [math] limit of the [math]-vector ferromagnet with self-avoiding random walk in linear polymers. We provide here strong numerical evidence that a similar isomorphism appears to emerge connecting the energy [math]-exponential distribution [math] (with [math] and [math]) optimizing, under simple constraints, the nonadditive entropy [math] with a specific geographic growth random model based on preferential attachment through exponentially distributed weighted links, [math] being the characteristic weight.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-19T11:38:50Z
      DOI: 10.1063/5.0090864
       
  • Propagation failure in discrete reaction–diffusion system based on
           the butterfly bifurcation

    • Free pre-print version: Loading...

      Authors: K. Rohe, J. Cisternas
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Reaction–diffusion systems are used in biology, chemistry, and physics to model the interaction of spatially distributed species. Particularly of interest is the spatial replacement of one equilibrium state by another, depicted as traveling waves or fronts. Their profiles and traveling velocity depend on the nonlinearities in the reaction term and on spatial diffusion. If the reaction occurs at regularly spaced points, the velocities also depend on lattice structures and the orientation of the traveling front. Interestingly, there is a wide region of parameters where the speeds become zero and the fronts do not propagate. In this paper, we focus on systems with three stable coexisting equilibrium states that are described by the butterfly bifurcation and study to what extent the three possible 1D traveling fronts suffer from propagation failure. We demonstrate that discreteness of space affects the three fronts differently. Regions of propagation failure add a new layer of complexity to the butterfly diagram. The analysis is extended to planar fronts traveling through different orientations in regular 2D lattices. Both propagation failure and the existence of preferred orientations play a role in the transient and long-time evolution of 2D patterns.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-18T02:09:56Z
      DOI: 10.1063/5.0086239
       
  • An optimal control method to compute the most likely transition path for
           stochastic dynamical systems with jumps

    • Free pre-print version: Loading...

      Authors: Wei Wei, Ting Gao, Xiaoli Chen, Jinqiao Duan
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Many complex real world phenomena exhibit abrupt, intermittent, or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian Lévy noise. Among these complex phenomena, the most likely transition paths between metastable states are important since these rare events may have a high impact in certain scenarios. Based on the large deviation principle, the most likely transition path could be treated as the minimizer of the rate function upon paths that connect two points. One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian Lévy noise is that the associated rate function cannot be explicitly expressed by paths. For this reason, we formulate an optimal control problem to obtain the optimal state as the most likely transition path. We then develop a neural network method to solve this issue. Several experiments are investigated for both Gaussian and non-Gaussian cases.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-18T02:09:55Z
      DOI: 10.1063/5.0093924
       
  • Impulsive feedback control of birhythmicity: Theory and experiment

    • Free pre-print version: Loading...

      Authors: Debabrata Biswas, Tanmoy Banerjee, Jürgen Kurths
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We study the dynamic control of birhythmicity under an impulsive feedback control scheme where the feedback is made ON for a certain rather small period of time and for the rest of the time, it is kept OFF. We show that, depending on the height and width of the feedback pulse, the system can be brought to any of the desired limit cycles of the original birhythmic oscillation. We derive a rigorous analytical condition of controlling birhythmicity using the harmonic decomposition and energy balance methods. The efficacy of the control scheme is investigated through numerical analysis in the parameter space. We demonstrate the robustness of the control scheme in a birhythmic electronic circuit where the presence of noise and parameter fluctuations are inevitable. Finally, we demonstrate the applicability of the control scheme in controlling birhythmicity in diverse engineering and biochemical systems and processes, such as an energy harvesting system, a glycolysis process, and a p53-mdm2 network.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-18T02:09:54Z
      DOI: 10.1063/5.0089616
       
  • Entrainment, stopping, and transmission of microwave solitons of
           

    • Free pre-print version: Loading...

      Authors: A. S. Sergeev, L. A. Yurovskiy, N. S. Ginzburg, I. V. Zotova, I. V. Zheleznov, R. M. Rozental, A. A. Rostuntsova, N. M. Ryskin
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Based on numerical simulations of a boundary problem, we study various scenarios of microwave soliton formation in the process of cyclotron resonance interaction of a short electromagnetic pulse with a counter-propagating initially rectilinear electron beam taking into account the relativistic dependence of the cyclotron frequency on the electrons’ energy. When a certain threshold in the pulse energy is exceeded, the incident pulse can propagate without damping in the absorbing beam, similar to the effect of self-induced transparency in optics. However, mutual motion of the wave and electrons can lead to some novel effects. For relatively small energy of the incident pulse, the microwave soliton is entrained by the electron beam opposite to the direction of the wave's group velocity. With an increase in the pulse energy, soliton stopping occurs. This regime is characterized by the close-to-zero pulse velocity and can be interpreted as a variety of the “light stopping.” High-energy microwave solitons propagate in the direction of the unperturbed group velocity. Their amplitude may exceed the amplitude of the incident pulse, i.e., nonlinear self-compression takes place. A further increase in the incident energy leads to the formation of additional high-order solitons whose behavior is similar to that of the first-order ones. The characteristics of each soliton (its amplitude and duration) correspond to analytical two-parametric soliton solutions that are to be found from consideration of the unbounded problem.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-17T11:52:49Z
      DOI: 10.1063/5.0087408
       
  • Conditional Gaussian nonlinear system: A fast preconditioner and a cheap
           surrogate model for complex nonlinear systems

    • Free pre-print version: Loading...

      Authors: Nan Chen, Yingda Li, Honghu Liu
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Developing suitable approximate models for analyzing and simulating complex nonlinear systems is practically important. This paper aims at exploring the skill of a rich class of nonlinear stochastic models, known as the conditional Gaussian nonlinear system (CGNS), as both a cheap surrogate model and a fast preconditioner for facilitating many computationally challenging tasks. The CGNS preserves the underlying physics to a large extent and can reproduce intermittency, extreme events, and other non-Gaussian features in many complex systems arising from practical applications. Three interrelated topics are studied. First, the closed analytic formulas of solving the conditional statistics provide an efficient and accurate data assimilation scheme. It is shown that the data assimilation skill of a suitable CGNS approximate forecast model outweighs that by applying an ensemble method even to the perfect model with strong nonlinearity, where the latter suffers from filter divergence. Second, the CGNS allows the development of a fast algorithm for simultaneously estimating the parameters and the unobserved variables with uncertainty quantification in the presence of only partial observations. Utilizing an appropriate CGNS as a preconditioner significantly reduces the computational cost in accurately estimating the parameters in the original complex system. Finally, the CGNS advances rapid and statistically accurate algorithms for computing the probability density function and sampling the trajectories of the unobserved state variables. These fast algorithms facilitate the development of an efficient and accurate data-driven method for predicting the linear response of the original system with respect to parameter perturbations based on a suitable CGNS preconditioner.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-17T11:52:48Z
      DOI: 10.1063/5.0081668
       
  • Sandpile cascades on oscillator networks: The BTW model meets Kuramoto

    • Free pre-print version: Loading...

      Authors: Guram Mikaberidze, Raissa M. D’Souza
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Cascading failures abound in complex systems and the Bak–Tang–Weisenfeld (BTW) sandpile model provides a theoretical underpinning for their analysis. Yet, it does not account for the possibility of nodes having oscillatory dynamics, such as in power grids and brain networks. Here, we consider a network of Kuramoto oscillators upon which the BTW model is unfolding, enabling us to study how the feedback between the oscillatory and cascading dynamics can lead to new emergent behaviors. We assume that the more out-of-sync a node is with its neighbors, the more vulnerable it is and lower its load-carrying capacity accordingly. Also, when a node topples and sheds load, its oscillatory phase is reset at random. This leads to novel cyclic behavior at an emergent, long timescale. The system spends the bulk of its time in a synchronized state where load builds up with minimal cascades. Yet, eventually, the system reaches a tipping point where a large cascade triggers a “cascade of larger cascades,” which can be classified as a dragon king event. The system then undergoes a short transient back to the synchronous, buildup phase. The coupling between capacity and synchronization gives rise to endogenous cascade seeds in addition to the standard exogenous ones, and we show their respective roles. We establish the phenomena from numerical studies and develop the accompanying mean-field theory to locate the tipping point, calculate the load in the system, determine the frequency of the long-time oscillations, and find the distribution of cascade sizes during the buildup phase.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-16T11:40:07Z
      DOI: 10.1063/5.0095094
       
  • Tiered synchronization in coupled oscillator populations with interaction
           delays and higher-order interactions

    • Free pre-print version: Loading...

      Authors: Per Sebastian Skardal, Can Xu
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We study synchronization in large populations of coupled phase oscillators with time delays and higher-order interactions. With each of these effects individually giving rise to bistability between incoherence and synchronization via subcriticality at the onset of synchronization and the development of a saddle node, we find that their combination yields another mechanism behind bistability, where supercriticality at onset may be maintained; instead, the formation of two saddle nodes creates tiered synchronization, i.e., bistability between a weakly synchronized state and a strongly synchronized state. We demonstrate these findings by first deriving the low dimensional dynamics of the system and examining the system bifurcations using a stability and steady-state analysis.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-16T11:40:05Z
      DOI: 10.1063/5.0086305
       
  • Dynamic community detection over evolving networks based on the optimized
           deep graph infomax

    • Free pre-print version: Loading...

      Authors: Hao Liu, Langzhou He, Fan Zhang, Zhen Wang, Chao Gao
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-based methods stand out by embedding the global network structure and properties into a low-dimensional representation for community detection. However, such kinds of methods can only be utilized at each single time step independently. As a consequence, the information of all time steps requires to be stored, which increases the computational cost. Besides this, the neighbors of target nodes are considered equally when aggregating nodes in networks, which omits the local structural feature of networks and influences the accuracy of node representation. To overcome such shortcomings, this paper proposes a novel optimized dynamic deep graph infomax (ODDGI) method for dynamic community detection. Since the recurrent neural network (RNN) can capture the dynamism of networks while avoiding storing all information of dynamic networks, our ODDGI utilizes RNN to update deep graph infomax parameters, and thus, there is no need to store the knowledge of nodes in full time span anymore. Moreover, the importance of nodes is considered using similarity aggregation strategy to improve the accuracy of node representation. The experimental results on both the real-world and synthetic networks prove that our method surpasses other state-of-the-art dynamic community detection algorithms in clustering accuracy and stability.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-13T02:54:56Z
      DOI: 10.1063/5.0086795
       
  • Erratum:“Quasi-objective coherent structure diagnostics from single
           trajectories” [Chaos 31, 043131 (2021)]

    • Free pre-print version: Loading...

      Authors: George Haller, Nikolas Aksamit, Alex P. Encinas-Bartos
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.

      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-10T05:27:22Z
      DOI: 10.1063/5.0090124
       
  • An improved framework for the dynamic likelihood filtering approach to
           data assimilation

    • Free pre-print version: Loading...

      Authors: Dallas Foster, Juan M. Restrepo
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We propose improvements to the Dynamic Likelihood Filter (DLF), a Bayesian data assimilation filtering approach, specifically tailored to wave problems. The DLF approach was developed to address the common challenge in the application of data assimilation to hyperbolic problems in the geosciences and in engineering, where observation systems are sparse in space and time. When these observations have low uncertainties, as compared to model uncertainties, the DLF exploits the inherent nature of information and uncertainties to propagate along characteristics to produce estimates that are phase aware as well as amplitude aware, as would be the case in the traditional data assimilation approach. Along characteristics, the stochastic partial differential equations underlying the linear or nonlinear stochastic dynamics are differential equations. This study focuses on developing the explicit challenges of relating dynamics and uncertainties in the Eulerian and Lagrangian frames via dynamic Gaussian processes. It also implements the approach using the ensemble Kalman filter (EnKF) and compares the DLF approach to the conventional one with respect to wave amplitude and phase estimates in linear and nonlinear wave problems. Numerical comparisons show that the DLF/EnKF outperforms the EnKF estimates, when applied to linear and nonlinear wave problems. This advantage is particularly noticeable when sparse, low uncertainty observations are used.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-10T05:26:48Z
      DOI: 10.1063/5.0083071
       
  • Dynamic stability of electric power grids: Tracking the interplay of the
           network structure, transmission losses, and voltage dynamics

    • Free pre-print version: Loading...

      Authors: Philipp C. Böttcher, Dirk Witthaut, Leonardo Rydin Gorjão
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Dynamic stability is imperative for the operation of the electric power system. This article provides analytical results and effective stability criteria focusing on the interplay of network structures and the local dynamics of synchronous machines. The results are based on an extensive linear stability analysis of the third-order model for synchronous machines, comprising the classical power-swing equations and the voltage dynamics. The article addresses the impact of Ohmic losses, which are important in distribution and microgrids but often neglected in analytical studies. We compute the shift of the stability boundaries to leading order, and thus provide a detailed qualitative picture of the impact of Ohmic losses. A subsequent numerical study of the criteria is presented, without and with resistive terms, to test how tight the derived analytical results are.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-10T05:25:29Z
      DOI: 10.1063/5.0082712
       
  • Wave-packet spreading in disordered soft architected structures

    • Free pre-print version: Loading...

      Authors: A. Ngapasare, G. Theocharis, O. Richoux, Ch. Skokos, V. Achilleos
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We study the dynamical and chaotic behavior of a disordered one-dimensional elastic mechanical lattice, which supports translational and rotational waves. The model used in this work is motivated by the recent experimental results of Deng et al. [Nat. Commun. 9, 1 (2018)]. This lattice is characterized by strong geometrical nonlinearities and the coupling of two degrees-of-freedom (DoFs) per site. Although the linear limit of the structure consists of a linear Fermi–Pasta–Ulam–Tsingou lattice and a linear Klein–Gordon (KG) lattice whose DoFs are uncoupled, by using single site initial excitations on the rotational DoF, we evoke the nonlinear coupling between the system’s translational and rotational DoFs. Our results reveal that such coupling induces rich wave-packet spreading behavior in the presence of strong disorder. In the weakly nonlinear regime, we observe energy spreading only due to the coupling of the two DoFs (per site), which is in contrast to what is known for KG lattices with a single DoF per lattice site, where the spreading occurs due to chaoticity. Additionally, for strong nonlinearities, we show that initially localized wave-packets attain near ballistic behavior in contrast to other known models. We also reveal persistent chaos during energy spreading, although its strength decreases in time as quantified by the evolution of the system’s finite-time maximum Lyapunov exponent. Our results show that flexible, disordered, and strongly nonlinear lattices are a viable platform to study energy transport in combination with multiple DoFs (per site), also present an alternative way to control energy spreading in heterogeneous media.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-09T03:37:03Z
      DOI: 10.1063/5.0089055
       
  • Data driven soliton solution of the nonlinear Schrödinger equation with
           certain [math]-symmetric potentials via deep learning

    • Free pre-print version: Loading...

      Authors: J. Meiyazhagan, K. Manikandan, J. B. Sudharsan, M. Senthilvelan
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We investigate the physics informed neural network method, a deep learning approach, to approximate soliton solution of the nonlinear Schrödinger equation with parity time symmetric potentials. We consider three different parity time symmetric potentials, namely, Gaussian, periodic, and Rosen–Morse potentials. We use the physics informed neural network to solve the considered nonlinear partial differential equation with the above three potentials. We compare the predicted result with the actual result and analyze the ability of deep learning in solving the considered partial differential equation. We check the ability of deep learning in approximating the soliton solution by taking the squared error between real and predicted values. Further, we examine the factors that affect the performance of the considered deep learning method with different activation functions, namely, ReLU, sigmoid, and tanh. We also use a new activation function, namely, sech, which is not used in the field of deep learning, and analyze whether this new activation function is suitable for the prediction of soliton solution of the nonlinear Schrödinger equation for the aforementioned parity time symmetric potentials. In addition to the above, we present how the network’s structure and the size of the training data influence the performance of the physics informed neural network. Our results show that the constructed deep learning model successfully approximates the soliton solution of the considered equation with high accuracy.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-06T02:57:51Z
      DOI: 10.1063/5.0086038
       
  • Coexistence of infinitely many patterns and their control in heterogeneous
           coupled neurons through a multistable memristive synapse

    • Free pre-print version: Loading...

      Authors: Zeric Njitacke Tabekoueng, Sishu Shankar Muni, Théophile Fonzin Fozin, Gervais Dolvis Leutcho, Jan Awrejcewicz
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      The phenomenon of hidden heterogeneous extreme multistability is rarely reported in coupled neurons. This phenomenon is investigated in this contribution using a model of a 2D FitzHugh–Nagumo neuron coupled with a 3D Hindmarsh–Rose neuron through a multistable memristive synapse. The investigation of the equilibria revealed that the coupled neuron model is equilibrium free and, thus, displays a hidden dynamics. Some traditional nonlinear analysis tools are used to demonstrate that the heterogeneous neuron system is able to exhibit the coexistence of an infinite number of electrical activities involving both periodic and chaotic patterns. Of particular interest, a noninvasive control method is applied to suppress all the periodic coexisting activities, while preserving only the desired chaotic one. Finally, an electronic circuit of the coupled neurons is designed in the PSpice environment and used to further support some results of the theoretical investigations.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-06T02:56:51Z
      DOI: 10.1063/5.0086182
       
  • Hypergraph assortativity: A dynamical systems perspective

    • Free pre-print version: Loading...

      Authors: Nicholas W. Landry, Juan G. Restrepo
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      The largest eigenvalue of the matrix describing a network’s contact structure is often important in predicting the behavior of dynamical processes. We extend this notion to hypergraphs and motivate the importance of an analogous eigenvalue, the expansion eigenvalue, for hypergraph dynamical processes. Using a mean-field approach, we derive an approximation to the expansion eigenvalue in terms of the degree sequence for uncorrelated hypergraphs. We introduce a generative model for hypergraphs that includes degree assortativity, and use a perturbation approach to derive an approximation to the expansion eigenvalue for assortative hypergraphs. We define the dynamical assortativity, a dynamically sensible definition of assortativity for uniform hypergraphs, and describe how reducing the dynamical assortativity of hypergraphs through preferential rewiring can extinguish epidemics. We validate our results with both synthetic and empirical datasets.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-06T02:40:31Z
      DOI: 10.1063/5.0086905
       
  • High precision reconstruction of silicon photonics chaos with stacked
           CNN-LSTM neural networks

    • Free pre-print version: Loading...

      Authors: Wei Cheng, Junbo Feng, Yan Wang, Zheng Peng, Hao Cheng, Xiaodong Ren, Yubei Shuai, Shengyin Zang, Hao Liu, Xun Pu, Junbo Yang, Jiagui Wu
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Silicon-based optical chaos has many advantages, such as compatibility with complementary metal oxide semiconductor (CMOS) integration processes, ultra-small size, and high bandwidth. Generally, it is challenging to reconstruct chaos accurately because of its initial sensitivity and high complexity. Here, a stacked convolutional neural network (CNN)-long short-term memory (LSTM) neural network model is proposed to reconstruct optical chaos with high accuracy. Our network model combines the advantages of both CNN and LSTM modules. Further, a theoretical model of integrated silicon photonics micro-cavity is introduced to generate chaotic time series for use in chaotic reconstruction experiments. Accordingly, we reconstructed the one-dimensional, two-dimensional, and three-dimensional chaos. The experimental results show that our model outperforms the LSTM, gated recurrent unit (GRU), and CNN models in terms of MSE, MAE, and R-squared metrics. For example, the proposed model has the best value of this metric, with a maximum improvement of 83.29% and 49.66%. Furthermore, 1D, 2D, and 3D chaos were all significantly improved with the reconstruction tasks.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-06T02:01:00Z
      DOI: 10.1063/5.0082993
       
  • Controlling the spontaneous firing behavior of a neuron with astrocyte

    • Free pre-print version: Loading...

      Authors: Tugba Palabas, Andre Longtin, Dibakar Ghosh, Muhammet Uzuntarla
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in the regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocytes on the spontaneous firing activity of a neuron through a combined model that includes a neuron–astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in the occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-06T01:59:30Z
      DOI: 10.1063/5.0093234
       
  • Phase-based causality analysis with partial mutual information from mixed
           embedding

    • Free pre-print version: Loading...

      Authors: Ioannis Vlachos, Dimitris Kugiumtzis, Milan Paluš
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Instantaneous phases extracted from multivariate time series can retain information about the relationships between the underlying mechanisms that generate the series. Although phases have been widely used in the study of nondirectional coupling and connectivity, they have not found similar appeal in the study of causality. Herein, we present a new method for phase-based causality analysis, which combines ideas from the mixed embedding technique and the information-theoretic approach to causality in coupled oscillatory systems. We then use the introduced method to investigate causality in simulated datasets of bivariate, unidirectionally paired systems from combinations of Rössler, Lorenz, van der Pol, and Mackey–Glass equations. We observe that causality analysis using the phases can capture the true causal relation for coupling strength smaller than the analysis based on the amplitudes can capture. On the other hand, the causality estimation based on the phases tends to have larger variability, which is attributed more to the phase extraction process than the actual phase-based causality method. In addition, an application on real electroencephalographic data from an experiment on elicited human emotional states reinforces the usefulness of phases in causality identification.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-05T05:07:57Z
      DOI: 10.1063/5.0087910
       
  • Efficient routing for spatial networks

    • Free pre-print version: Loading...

      Authors: Hong Lin, Yongxiang Xia, Yuanyuan Liang
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      In many complex networks, the main task is to transfer load from sources to destinations. Therefore, the network throughput is a very important indicator to measure the network performance. In order to improve the network throughput, researchers have proposed many effective routing strategies. Spatial networks, as a class of complex networks, exist widely in the real-world. However, the existing routing strategies in complex networks cannot achieve good results when applied in spatial networks. Therefore, in this paper, we propose a new degree-location ([math]) routing strategy to improve the throughput of spatial networks. In this routing strategy, the load transmitted from sources to destinations will bypass the nodes with high degrees and the nodes located close to the center of region. Simulations on homogeneous and heterogeneous spatial networks show that the [math] routing strategy proposed in this paper can effectively improve the throughput of the network. The result of this paper can help the routing design of spatial networks and may find applications in many real-world spatial networks to improve the transmission performance.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-05T05:06:13Z
      DOI: 10.1063/5.0091976
       
  • Multiplex network disintegration strategy inference based on deep network
           representation learning

    • Free pre-print version: Loading...

      Authors: Chengyi Zeng, Lina Lu, Hongfu Liu, Jing Chen, Zongtan Zhou
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Multiplex networks have attracted more and more attention because they can model the coupling of network nodes between layers more accurately. The interaction of nodes between layers makes the attack effect on multiplex networks not simply a linear superposition of the attack effect on single-layer networks, and the disintegration of multiplex networks has become a research hotspot and difficult. Traditional multiplex network disintegration methods generally adopt approximate and heuristic strategies. However, these two methods have a number of drawbacks and fail to meet our requirements in terms of effectiveness and timeliness. In this paper, we develop a novel deep learning framework, called MINER (Multiplex network disintegration strategy Inference based on deep NEtwork Representation learning), which transforms the disintegration strategy inference of multiplex networks into the encoding and decoding process based on deep network representation learning. In the encoding process, the attention mechanism encodes the coupling relationship of corresponding nodes between layers, and reinforcement learning is adopted to evaluate the disintegration action in the decoding process. Experiments indicate that the trained MINER model can be directly transferred and applied to the disintegration of multiplex networks with different scales. We extend it to scenarios that consider node attack cost constraints and also achieve excellent performance. This framework provides a new way to understand and employ multiplex networks.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-05T03:10:36Z
      DOI: 10.1063/5.0075575
       
  • Predictor feedback models for stick balancing with delay mismatch and
           sensory dead zones

    • Free pre-print version: Loading...

      Authors: Dalma J. Nagy, Tamás Insperger
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Human stick balancing is investigated in terms of reaction time delay and sensory dead zones for position and velocity perception using a special combination of delayed state feedback and mismatched predictor feedback as a control model. The corresponding mathematical model is a delay-differential equation with event-driven switching in the control action. Due to the sensory dead zones, initial conditions of the actual state cannot always be provided for an internal-model-based prediction, which indicates that (1) perfect prediction is not possible and (2) the delay in the switching condition cannot be compensated. The imperfection of the predictor is described by the delay mismatch, which is treated as a lumped parameter that creates a transition between perfect predictor feedback (zero delay mismatch) and delayed state feedback (mismatch equal to switching delay). The maximum admissible switching delay (critical delay) is determined numerically based on a practical stabilizability concept. This critical delay is compared to a realistic reference value of 230 ms in order to assess the possible regions of the threshold values for position and velocity perception. The ratio of the angular position and angular velocity for 44 successful balancing trials by 8 human subjects was used to validate the numerical results. Comparison of actual human stick balancing data and numerical simulations based on the mismatched predictor feedback model provided a plausible range of parameters: position detection threshold 1°, velocity detection threshold between 4.24 and 9.35°/s, and delay mismatch around 100–150 ms.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-05T03:08:07Z
      DOI: 10.1063/5.0087019
       
  • Limit cycles in Filippov systems having a circle as switching manifold

    • Free pre-print version: Loading...

      Authors: Jaume Llibre, Marco Antonio Teixeira
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      It is known that planar discontinuous piecewise linear differential systems separated by a straight line have no limit cycles when both linear differential systems are centers. Here, we study the limit cycles of the planar discontinuous piecewise linear differential systems separated by a circle when both linear differential systems are centers. Our main results show that such discontinuous piecewise differential systems can have zero, one, two, or three limit cycles, but no more limit cycles than three.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-03T02:38:52Z
      DOI: 10.1063/5.0082607
       
  • Filament dynamics in vertical confined chemical gardens

    • Free pre-print version: Loading...

      Authors: Luis A. M. Rocha, Julyan H. E. Cartwright, Silvana S. S. Cardoso
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      When confined to a Hele-Shaw cell, chemical gardens can grow as filaments, narrow structures with an erratic and tortuous trajectory. In this work, the methodology applied to studies with horizontal Hele-Shaw cells is adapted to a vertical configuration, thus introducing the effect of buoyancy into the system. The motion of a single filament tip is modeled by taking into account its internal pressure and the variation of the concentration of precipitate that constitutes the chemical garden membrane. While the model shows good agreement with the results, it also suggests that the concentration of the host solution of sodium silicate also plays a role in the growth of the structures despite being in stoichiometric excess.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-03T02:37:52Z
      DOI: 10.1063/5.0085834
       
  • Spatiotemporal dynamics of biocrust and vegetation on sand dunes

    • Free pre-print version: Loading...

      Authors: H. Yizhaq, Y. Ashkenazy
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      We propose a model to study at the first time the spatiotemporal dynamics of the coupling between biocrust and vegetation cover on sand dunes; previous studies modeled the temporal dynamics of vegetation-biocrust-sand system while other focused only on the spatiotemporal dynamics of vegetation on sand dunes, excluding the effect of biocrust. The model consists of two coupled partial nonlinear differential equations and includes diffusion and advection terms for modeling the dispersal of vegetation and biocrust and the effect of wind on them. In the absence of spatial variability, the model exhibits self-sustained relaxation oscillations and regimes of bistability–the first state is dominated by biocrust and the second by vegetation. We concentrate on the one-dimensional dynamics of the model and show that the front that connects these two states propagates mainly due to the wind advection. In the oscillatory regime the front propagation is complex and very interesting compared to the non-spatial relaxation oscillations. For low wind DP (drift potential) values, a series of spatially oscillatory domains develops as the front advances downwind. These domains form due to the oscillations of the spatially homogeneous states away from the front. However, for higher DP values, the dynamics is much more complex, becoming very sensitive to the initial conditions and exhibiting an irregular spatial pattern as small domains are created and annihilated during the front advance. The irregular spatiotemporal dynamics reported here seems to be unique, at least in the context of vegetation dynamics and possibly also in context of other dynamical systems.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-02T12:27:31Z
      DOI: 10.1063/5.0087296
       
  • Propagation dynamics and control policies of COVID-19 pandemic at early
           stages: Implications on future resurgence response

    • Free pre-print version: Loading...

      Authors: Ni Dong, Xiangyang Guan, Jin Zhang, Hanchu Zhou, Jie Zhang, Xiaobo Liu, Yichen Sun, Pengpeng Xu, Qin Li, Xingjie Hao
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      The spreading of novel coronavirus (SARS-CoV-2) has gravely impacted the world in the last year and a half. Understanding the spatial and temporal patterns of how it spreads at the early stage and the effectiveness of a governments' immediate response helps our society prepare for future COVID-19 waves or the next pandemic and contain it before the spreading gets out of control. In this article, a susceptible-exposed-infectious-removed model is used to model the city-to-city spreading patterns of the disease at the early stage of its emergence in China (from December 2019 to February 2020). Publicly available reported case numbers in 312 Chinese cities and between-city mobility data are leveraged to estimate key epidemiological characteristics, such as the transmission rate and the number of infectious people for each city. It is discovered that during any given time period, there are always only a few cities that are responsible for spreading the disease to other cities. We term these few cities as transmission centers. The spatial and temporal changes in transmission centers demonstrate predictable patterns. Moreover, rigorously designed experiments show that in controlling the disease spread in a city, non-pharmaceutical interventions (NPIs) implemented at transmission centers are more effective than the NPI implemented in the city itself. These findings have implications on the control of an infectious disease at the early stage of its spreading: implementing NPIs at transmission centers at early stages is effective in controlling the spread of infectious diseases.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-02T10:54:47Z
      DOI: 10.1063/5.0076255
       
  • Logarithmic spiral solutions of the Kopell–Howard lambda–omega
           reaction–diffusion equations

    • Free pre-print version: Loading...

      Authors: William C. Troy
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Our investigation of logarithmic spirals is motivated by disparate experimental results: (i) the discovery of logarithmic spiral shaped precipitate formation in chemical garden experiments. Understanding precipitate formation in chemical gardens is important since analogous precipitates form in deep ocean hydrothermal vents, where conditions may be compatible with the emergence of life. (ii) The discovery that logarithmic spiral shaped waves of spreading depression can spontaneously form and cause macular degeneration in hypoglycemic chick retina. The role of reaction–diffusion mechanisms in spiral formation in these diverse experimental settings is poorly understood. To gain insight, we use the topological shooting to prove the existence of 0-bump stationary logarithmic spiral solutions, and rotating logarithmic spiral wave solutions, of the Kopell–Howard lambda–omega reaction–diffusion model.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-02T10:54:46Z
      DOI: 10.1063/5.0082736
       
  • A two-stage reconstruction method for complex networked system with hidden
           nodes

    • Free pre-print version: Loading...

      Authors: Wenfeng Deng, Chunhua Yang, Keke Huang, Wenhan Wu
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      Reconstructing the interacting topology from measurable data is fundamental to understanding, controlling, and predicting the collective dynamics of complex networked systems. Many methods have been proposed to address the basic inverse problem and have achieved satisfactory performance. However, a significant challenge arises when we attempt to decode the underlying structure in the presence of inaccessible nodes due to the partial loss of information. For the purpose of improving the accuracy of network reconstruction with hidden nodes, we developed a robust two-stage network reconstruction method for complex networks with hidden nodes from a small amount of observed time series data. Specifically, the proposed method takes full advantage of the natural sparsity of complex networks and the potential symmetry constraints in dynamic interactions. With robust reconstruction, we can not only locate the position of hidden nodes but also precisely recover the overall network structure on the basis of compensated nodal information. Extensive experiments are conducted to validate the effectiveness of the proposed method and superiority compared with ordinary methods. To some extent, this work sheds light on addressing the inverse problem, of which the system lacks complete exploration in the network science community.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-02T10:54:46Z
      DOI: 10.1063/5.0087740
       
  • Incorporating economic constraints for optimal control of immunizing
           infections

    • Free pre-print version: Loading...

      Authors: Yu-Jhe Huang, An-Tien Hsiao, Jonq Juang
      Abstract: Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 32, Issue 5, May 2022.
      It is well-known that the interruption of transmission of a disease can be achieved, provided the vaccinated population reaches a threshold depending on, among others, the efficacy of vaccines. The purpose of this paper is to address the optimal vaccination strategy by imposing the economic constraints. In particular, an [math] model used to describe the spreading of the disease in a well-mixed population and a cost function consisting of vaccination and infection costs are proposed. The well-definedness of the above-described modeling is provided. We were then able to provide an optimal strategy to minimize the cost for all parameters. In particular, the optimal vaccination level to minimize the cost can be completely characterized for all parameters. For instance, the optimal vaccination level can be classified by the magnitude of the failure rate of the vaccine with other parameters being given. Under these circumstances, the optimal strategy to minimize the cost is roughly to eliminate the disease locally (respectively, choose an economic optimum resulting in not to wipe out the disease completely or take no vaccination for anyone) provided the vaccine failure rate is relatively small (respectively, intermediate or large). Numerical simulations to illustrate our main results are also provided. Moreover, the data collected at the height of the Covid-19 pandemic in Taiwan are also numerically simulated to provide the corresponding optimal vaccination strategy.
      Citation: Chaos: An Interdisciplinary Journal of Nonlinear Science
      PubDate: 2022-05-02T10:54:45Z
      DOI: 10.1063/5.0083312
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 34.204.174.110
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-