Article
Engineering, Multidisciplinary
Lucia Valentina Gambuzza, Mattia Frasca, Francesco Sorrentino, Louis M. Pecora, Stefano Boccaletti
Summary: Symmetries play a crucial role in regulating collective dynamics in complex networks, and this study focuses on controlling network symmetries and enforcing patterned states of synchronization. By perturbing the original network connectivity with minimal changes, desirable clustering of nodes can be achieved. The stability conditions of enforced patterns are derived and the method's performance is illustrated with examples relevant to various practical scenarios.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Somnath Mondal, Ashok K. Mishra, Ruby Leung, Benjamin Cook
Summary: This study utilizes Complex Network analysis to investigate the topological characteristics of global drought events, revealing a highly heterogeneous connectivity structure among drought hotspot regions. The co-occurrence of droughts in multiple continents suggests the possibility of simultaneous large-scale droughts.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jianying Xiao, Jinde Cao, Jun Cheng, Shiping Wen, Ruimei Zhang, Shouming Zhong
Summary: This article focuses on the global synchronization and stability of fractional-order quaternion-valued neural networks, proposing multiple and flexible criteria based on the Lyapunov theory and new inequalities. The effectiveness of these criteria is demonstrated through numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Mathematics, Applied
Eugenio Urdapilleta
Summary: Inhibitory neurons form an extensive network in the cerebral cortex, playing a crucial role in the development of different rhythms. The transition from incoherent to synchronized state is important, and effective synaptic connectivity patterns may support this transition. Additionally, an adaptive mechanism has been built to rapidly generate the underlying structure of this network based on ongoing firing statistics.
Article
Business, Finance
Xiaoyu Wang, Yanlin Sun, Bin Peng
Summary: This paper applies the common Markov-switching panel data model to study the similarities and differences across China's provinces in terms of the timing of entering a contraction. It derives the national business cycles, classifies provinces into clusters based on input-output linkages, and finds that most idiosyncratic cluster contractions exhibit differences in timing around the national contractions. The paper also investigates the reasons for business cycle synchronization.
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianying Xiao, Shouming Zhong, Shiping Wen
Summary: This paper investigates the global Mittag-Leffler synchronization problem for fractional-order multidimension-valued BAM neural networks with general activation functions. The study establishes a unified model for the systems and derives criteria in unified form without decomposition by constructing new Lyapunov-Krasovskii functionals, combining two new inequalities and considering the use of easy controllers. The obtained criteria offer several advantages in terms of higher flexibility, more variety, smaller computation, and lower conservatism.
Article
Computer Science, Information Systems
Joannes Sam Mertens, Laura Galluccio, Giacomo Morabito
Summary: This work introduces the i-WSN League framework, which is a comprehensive hardware/software framework for distributed training and inference. The framework combines gossiping and clustering to adapt the operations executed by each node to its capabilities, aiming to minimize energy consumption in resource-limited nodes while preserving accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Mathematics, Applied
Alex Arenas, Antonio Garijo, Sergio Gomez, Jordi Villadelprat
Summary: This paper investigates a system of coupled oscillators described by the Kuramoto model, discussing the stability and uniqueness criteria of equilibrium solutions. The analysis provides valuable insights into the dynamics of coupled oscillators in this system.
Article
Multidisciplinary Sciences
Yuanzhao Zhang, Maxime Lucas, Federico Battiston
Summary: Higher-order networks are a powerful framework for modeling complex systems and their collective behavior. The choice between simplicial complexes and hypergraphs has a significant impact on the dynamics of the system.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Nick MacMackin, Lindsay Miller, Rupp Carriveau
Summary: Electric vehicles, photovoltaics, heat pumps, and energy storage are transforming electricity systems and presenting challenges for operators. The study examines the impact of these new technologies on local grids, revealing varying effects on load curves and network operations. Results show that strategies like alternate charging and energy storage can mitigate peak loads and prolong equipment life.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Review
Engineering, Mechanical
Haoyu Cao, Zonghua Liu
Summary: This study reveals that the local networks of cortical regions may show a completely different synchronization transition from the global brain network, and uncovers different synchronization transitions in forward and backward processes. Numerical simulations demonstrate that amplitude death is more likely to occur in larger local networks.
NONLINEAR DYNAMICS
(2022)
Article
Multidisciplinary Sciences
Christian Kuehn, Christian Bick
Summary: Critical transitions are observed in complex systems, such as synchronization in coupled oscillator networks and epidemic states in populations. The emergence of explosive first-order transitions has garnered attention when classical models are generalized with additional effects. Mathematical arguments suggest that the change in criticality is universally expected when a classical model is varied along a generic two-parameter family. Three explicit examples of this effect are illustrated in different physical systems.
Article
Automation & Control Systems
Feng Yang, Lihong Shi, Yan Liang, Litao Zheng
Summary: This paper investigates the global fusion estimation problem in clustered sensor networks with cross-correlated measurement noises. It proposes a globally indirect sequential measurement fusion estimation algorithm and proves the accuracy relations with batch fusion estimation and centralized Kalman fuser.
Article
Physics, Fluids & Plasmas
Serhiy Brezetsky, Patrycja Jaros, Roman Levchenko, Tomasz Kapitaniak, Yuri Maistrenko
Summary: This study reveals the complex behavior of chimeras in small networks of phase oscillators with inertia, exhibiting extreme sensitivity to initial conditions and parameter changes. The unpredictability of the system dynamics, characterized by heteroclinic switching and riddling basins of attractions, poses a challenge in the field of network sciences. The presence of stable phase-locked states or other stable chimeras in the system further adds to the uncertainty in its behavior.
Article
Computer Science, Artificial Intelligence
Jianying Xiao, Xiao Guo, Yongtao Li, Shiping Wen, Kaibo Shi, Yiqian Tang
Summary: This paper establishes a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs). The higher dimensional models for FOOVBAMNNs with general activation functions and special linear threshold functions are formulated. The system of FOOVBAMNNs is divided into four fractional-order complex-valued systems using the Cayley-Dichson construction, and the Caputo fractional derivative's character and BAM's interactive feature are properly dealt with. The general criteria for the global Mittag-Leffler synchronization problem of FOOVBAMNNs are obtained, and two numerical examples are presented to show the realizability and progress of the derived results.
Article
Multidisciplinary Sciences
Huawei Fan, Ling-Wei Kong, Xingang Wang, Alan Hastings, Ying-Cheng Lai
Summary: Transient synchronization behavior is discovered in spatial ecological networks, where different patterns of complete synchronization coexist and switch randomly due to intrinsic instability or noise. This phenomenon, known as 'synchronization within synchronization,' is determined by network symmetry and follows an algebraic scaling law for transient time distribution with a divergent average transient lifetime. Symmetry considerations can also be used to explain counterintuitive synchronization behaviors in ecological networks.
NATIONAL SCIENCE REVIEW
(2021)
Article
Mathematics, Applied
Yali Guo, Han Zhang, Liang Wang, Huawei Fan, Jinghua Xiao, Xingang Wang
Summary: The study found that systems A and B with different parameters can achieve good synchronization, but if they differ in dynamics, the reservoir computer generally fails to synchronize with system B.
Article
Mathematics, Applied
Bing-Wei Li, Yuan He, Ling-Dong Li, Lei Yang, Xingang Wang
Summary: Spiral wave chimeras (SWCs) are a new type of dynamical pattern that can be observed even in locally coupled systems, according to a study using an experimentally feasible model. SWCs may become unstable in scenarios of core breakup and core expansion, with the latter leading to the emergence of shadowed spirals where regular spiral waves are embedded in a completely disordered background.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Mathematics, Applied
Ya Wang, Dapeng Zhang, Liang Wang, Qing Li, Hui Cao, Xingang Wang
Summary: The pinning control of cluster synchronization is studied in a globally connected network of chaotic oscillators. Simulation results show that when the pinning strength exceeds a critical value, the oscillators are synchronized into two different clusters. The study suggests that the generalized method of master stability function (MSF) can predict the synchronization behaviors of pinned oscillators but fails to predict those of unpinned oscillators. The failure is attributed to the deformed synchronization manifold of the unpinned oscillators, which deviates from that of isolated oscillators under strong pinnings.
Article
Mathematics, Applied
Han Zhang, Huawei Fan, Yao Du, Liang Wang, Xingang Wang
Summary: A model-free approach is proposed for anticipating the occurrence of measure synchronization in coupled Hamiltonian systems using machine learning techniques, showing the capability to accurately predict the critical coupling and system order parameter variations.
Article
Physics, Multidisciplinary
Huawei Fan, Ya Wang, Xingang Wang
Summary: While topological symmetries are crucial for synchronization patterns in complex networks, it remains challenging to identify these symmetries in large networks. This study proposes an eigenvector-based analysis framework to identify synchronization patterns and investigate the emergence and transition of cluster synchronization states. The method can predict observable cluster synchronization states, critical couplings, and the sequence of these states without prior knowledge of network symmetries. The proposed framework is validated using different models of coupled chaotic oscillators, showing its efficacy and generality in studying synchronization patterns in large, complex networks.
FRONTIERS OF PHYSICS
(2023)
Article
Physics, Fluids & Plasmas
Liang Wang, Huawei Fan, Yafeng Wang, Jian Gao, Yueheng Lan, Jinghua Xiao, Xingang Wang
Summary: In the study of network synchronization, the question of how to allocate heterogeneous oscillators on a complex network for improved synchronization performance is addressed using a model-free technique of a feed-forward neural network. The measured synchronization performance data is used to train a machine that can predict the performance of new allocation schemes and find the optimal allocation scheme for synchronization from a large number of candidates.
Article
Mathematics, Applied
Ya Wang, Liang Wang, Huawei Fan, Jun Ma, Hui Cao, Xingang Wang
Summary: Recent in vivo experiments have shown that astrocytes, a type of glial cell previously thought to provide structural and metabolic support to neurons, actively participate in brain functions by regulating neural firing activities. In this study, the authors propose a complex neuron-astrocyte network model and investigate the role of astrocytes in regulating cluster synchronization behaviors of chaotic neurons. They find that a specific set of neurons form a synchronized cluster while the remaining neurons remain desynchronized. Moreover, the cluster switches between synchronous and asynchronous states in an intermittent fashion, known as the breathing cluster phenomenon. The authors conduct theoretical investigations on the synchronizability of the cluster and reveal that the cluster contents are determined by network symmetry, while the breathing of the cluster is attributed to the interplay between the neural network and the astrocyte.
Article
Physics, Fluids & Plasmas
Liang Wang, Huawei Fan, Jinghua Xiao, Yueheng Lan, Xingang Wang
Summary: By designing an artificial neural network of coupled phase oscillators and training it using reservoir computing in machine learning, it is found that properly trained oscillators synchronize into clusters with a power-law distribution. Additionally, the reservoir network always develops into the same critical state once properly trained, demonstrating its attractor nature in machine learning.
Article
Physics, Multidisciplinary
Huawei Fan, Liang Wang, Yao Du, Yafeng Wang, Jinghua Xiao, Xingang Wang
Summary: Transient dynamics plays an important role in the functionality and management of complex systems. This study shows that using the technique of reservoir computing in machine learning, it is possible to accurately predict the transient behaviors and critical points of stable-to-unstable transitions in complex dynamical systems, based on measured time series data.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Physics, Fluids & Plasmas
Han Zhang, Huawei Fan, Liang Wang, Xingang Wang
Summary: The study demonstrates that machine learning approach using reservoir computing technique can reconstruct the KAM dynamics diagram of Hamiltonian system, even when the Hamiltonian equations of motion governing the system dynamics are unknown. This method can not only predict the short-term evolution of the system state, but also replicate the entire KAM dynamics diagram with high precision by tuning a control parameter externally.
Article
Physics, Multidisciplinary
Huawei Fan, Ling-Wei Kong, Ying-Cheng Lai, Xingang Wang
Summary: In realistic systems of coupled oscillators, the onset of synchronization can be predicted through machine learning, specifically reservoir computing or echo state networks. Trained neural machines can accurately predict synchronization transitions by tuning control parameters, covering various synchronization behaviors and transition scenarios in coupled systems. The remarkable feature is the ability to predict transition points and hysteresis for systems exhibiting explosive transitions.
PHYSICAL REVIEW RESEARCH
(2021)
Article
Physics, Multidisciplinary
Liang Wang, Yali Guo, Ya Wang, Huawei Fan, Xingang Wang
PHYSICAL REVIEW RESEARCH
(2020)
Article
Physics, Multidisciplinary
Huawei Fan, Junjie Jiang, Chun Zhang, Xingang Wang, Ying-Cheng Lai
PHYSICAL REVIEW RESEARCH
(2020)
Review
Astronomy & Astrophysics
Wang XinGang
SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA
(2020)
