Publications (20)
Enhancing network synchronization by phase modulation
Huawei Fan, Ying-Cheng Lai, Shi-Xian Qu +1
Due to time delays in signal transmission and processing, phase lags are inevitable in realistic complex oscillator networks. Conventional wisdom is that phase lags are detrimental…
Inferring Attracting Basins of Power System with Machine Learning
Yao Du, Qing Li, Huawei Fan +3
Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurate…
Synchronization within synchronization: transients and intermittency in ecological networks
Huawei Fan, Ling-Wei Kong, Xingang Wang +2
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control. We uncover a type of transient synchronization b…
The Wigner function for Integer quantum Hall effect
Yao Wang, Qi-Ming Fu, Huawei Fan +2
Wigner's quasi-probability distribution function in phase space is a specialized representation of the density matrix, possessing significant physical importance. In this article,…
Explosive growth of bistability in a cavity magnonic system
Meng-Xia Bi, Huawei Fan, Wenting Wu +3
We conduct a theoretical investigation into explosive growth of bistability in a cavity magnonic system incorporating magnetic nonlinearity. In this system, the coupling between th…
Breathing cluster in complex neuron-astrocyte networks
Ya Wang, Liang Wang, Huawei Fan +3
Brain activities are featured by spatially distributed neural clusters of coherent firings and a spontaneous switching of the clusters between the synchrony and asynchrony states.…
Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model
Tianyi Wang, Huawei Fan, Yuanchao Shu +2
Large Language Models face an emerging and critical threat known as latency attacks. Because LLM inference is inherently expensive, even modest slowdowns can translate into substan…
Anticipating synchronization with machine learning
Huawei Fan, Ling-Wei Kong, Ying-Cheng Lai +1
In applications of dynamical systems, situations can arise where it is desired to predict the onset of synchronization as it can lead to characteristic and significant changes in t…
Learning Hamiltonian dynamics by reservoir computer
Han Zhang, Huawei Fan, Liang Wang +1
Reconstructing the KAM dynamics diagram of Hamiltonian system from the time series of a limited number of parameters is an outstanding question in nonlinear science, especially whe…
Inferring synchronizability of networked heterogenous oscillators with machine learning
Liang Wang, Huawei Fan, Yafeng Wang +4
In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogenous oscillators on a comp…
Reconstructing bifurcation diagrams of chaotic circuits with reservoir computing
Haibo Luo, Yao Du, Huawei Fan +3
Model-free reconstruction of the bifurcation diagrams of Chua's circuits by the technique of parameter-aware reservoir computing is investigated. We demonstrate that: (1) reservoir…
Growth, collapse, and self-organized criticality in complex networks
Yafeng Wang, Huawei Fan, Ying-Cheng Lai +1
To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as su…
Long-term prediction of chaotic systems with recurrent neural networks
Huawei Fan, Junjie Jiang, Chun Zhang +2
Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic…
Versatile Reservoir Computing for Heterogeneous Complex Networks
Yao Du, Huawei Fan, Xingang Wang
A new machine learning scheme, termed versatile reservoir computing, is proposed for sustaining the dynamics of heterogeneous complex networks. We show that a single, small-scale r…
Transfer learning of chaotic systems
Yali Guo, Han Zhang, Liang Wang +2
Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great i…
Sustaining the dynamics of Kuramoto model by adaptable reservoir computer
Haibo Luo, Mengru Wang, Yao Du +3
A scenario frequently encountered in real-world complex systems is the temporary failure of a few components. For systems whose functionality hinges on the collective dynamics of t…
Criticality in Reservoir Computer of Coupled Phase Oscillators
Liang Wang, Huawei Fan, Jinghua Xiao +2
Accumulating evidences show that the cerebral cortex is operating near a critical state featured by power-law size distribution of neural avalanche activities, yet evidence of this…
Learning the dynamics of coupled oscillators from transients
Huawei Fan, Liang Wang, Yao Du +3
Whereas the importance of transient dynamics to the functionality and management of complex systems has been increasingly recognized, most of the studies are based on models. Yet i…
Scalable synchronization cluster in networked chaotic oscillators
Huawei Fan, Yafeng Wang, Yao Du +2
Cluster synchronization in synthetic networks of coupled chaotic oscillators is investigated. It is found that despite the asymmetric nature of the network structure, a subset of t…
Controlling synchronous patterns in complex networks
Weijie Lin, Huawei Fan, Ying Wang +2
Although the set of permutation symmetries of a complex network can be very large, few of the symmetries give rise to stable synchronous patterns. Here we present a new framework a…