Wei Lin

Wei Lin
Fudan University · Research Institute of Intelligent Complex Systems and School of Mathematical Sciences

PhD

About

146
Publications
24,057
Reads
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2,249
Citations
Introduction
I am a professor of applied mathematics, interested in nonlinear dynamical systems and complex networks, bifurcation and chaos theory, hybrid and temporal (switching) structures, random dynamics, adaptive control and synchronization, time series and causality analytics, parameters/delays estimation, models/networks identification, data assimilation, artificial neural networks, and all their applications to computational systems biology and brain-inspired intelligence.
Additional affiliations
January 2016 - present
Fudan University
Position
  • Professor (Full)
December 2009 - present
Fudan University
Position
  • Professor (Full)
May 2007 - April 2013
Chinese Academy of Sciences
Position
  • Researcher
Education
September 1998 - December 2002
Fudan University
Field of study
  • Applied Mathematics
September 1994 - July 1998
Fudan University
Field of study
  • Applied Mathematics and Cybernetics
September 1988 - July 1994
The Second Secondary School affiliated to East China Normal University
Field of study

Publications

Publications (146)
Article
Full-text available
Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focused either on open-loop control strategies and their energy consumptions, or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical unde...
Article
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Biorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore, calibrating both anomalous frequency and amplitude of biorhythm prevents physiological dysfunctions or diseases. However, many works were d...
Article
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Recent investigations reveal that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant where the systems change its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of the evolving systems and...
Article
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This article, based on the typical discrete-time semi-martingale convergence theorem, establishes several generalized versions of invariance principle for describing the long-term dynamical behaviors of discrete-time stochastic dynamical systems. These principles are suitable for investigating the dynamics in autonomous or non-autonomous systems an...
Article
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Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mecha...
Article
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Coordinated directional switches often emerge in moving biological groups replete with individual-level interactions. Recent self-propelled particles models can somewhat mimic the patterns of directional switches, but they usually do not include the effects of time delays in the interactions. Here, we focus on investigating the influence of time-de...
Article
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Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correcti...
Article
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Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
Data
Supplementary Material Appendices to “Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks with Intra-correlative and Inter-independent Connections”, https://doi.org/10.3389/fphys.2022.915511
Article
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Real neural system usually contains two types of neurons, i.e., excitatory neurons and inhibitory ones. Analytical and numerical interpretation of dynamics induced by different types of interactions among the neurons of two types is beneficial to understanding those physiological functions of the brain. Here, we articulate a model of noise-perturbe...
Article
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The mathematical framework of stochastically adaptive feedback control, which is generally applicable to significant problems in nonlinear dynamics such as stabilization and synchronization, has been previously established but only for systems whose vector fields satisfy the global Lipschitzian condition. Nonlinear dynamical systems arising from ph...
Preprint
Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mecha...
Conference Paper
Full-text available
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
Article
Full-text available
The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in Chi...
Article
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The COVID-19 pandemic has adversely affected the entire world. The effective implementation of vaccination strategy is critical to prevent the resurgence of the pandemic, especially during large-scale population migration. We establish a multiple patch coupled model based on the transportation network among the 31 provinces in China, under the comb...
Preprint
Full-text available
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
Article
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Air travel has been one of the hardest hit industries of COVID-19, with many flight cancellations and airport closures as a consequence. By analysing structural characteristics of the Official Aviation Guide flight data, we show that this resulted in an increased average distance between airports, and in an increased number of long-range routes. Ba...
Article
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The mechanisms inducing unpredictably directional switches in collective and moving biological entities are largely unclear. Deeply understanding such mechanisms is beneficial to delicate design of biologically-inspired devices with particular functions. Here, articulating a framework that integrates data-driven, analytical, and numerical methods,...
Article
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In this article, we investigate a type of biological tissue formation system with a random structure of reaction or/and diffusion, analyzing the connection with the results obtained in [Rajapakse & Smale, 2017a] for the corresponding deterministic systems and showing the major difference from these results. Interestingly, we find a dynamical phenom...
Preprint
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Realized vaccine efficacy in population is highly different from the individual vaccine efficacy measured in clinical trial. The realized vaccine efficacy in population is substantially affected by the vaccine age-stratified prioritization strategy, population age-structure, non-pharmaceutical intervention (NPI). We proposed a population vaccine ef...
Article
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In this article, we investigate the dynamics of non-Bayesian social learning model with periodically switching structures. Unlike the strongly connectedness conditions set for the temporal connecting networks of the non-Bayesian social learning to guarantee its convergence in the literature, our model configurations are essentially relaxed in a man...
Preprint
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Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here...
Article
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In this paper, we present an adaptive scheme involving heterogeneous delay interactions to suppress synchronization in a large population of oscillators. We analytically investigate the incoherent state stability regions for several specific kinds of distributions for delays. Interestingly, we find that, among the distributions that we discuss, the...
Article
Synchronization phenomena occur among populations of interacting elements and is of great importance for the functionality of several types of complex systems. Much effort has been devoted to understanding its emergence especially on the Kuramoto model, and the research now on coupled oscillators takes advantage of the recent theory of the interpla...
Conference Paper
Full-text available
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here...
Article
Full-text available
Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatm...
Article
The dynamical and structural aspects of cluster synchronization (CS) in complex systems have been intensively investigated in recent years. Here, we study CS of dynamical systems with intra- and inter-cluster couplings. We exploit new metrics that describe the performance of such systems and evaluate them as a function of the strength of the coupli...
Article
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A complex system’s structural–dynamical interplay plays a profound role in determining its collective behavior. Irregular behavior in the form of macroscopic chaos, for instance, can be potentially exhibited by the Kuramoto model of coupled phase oscillators at intermediate coupling strength with frequency assortativity and this behavior is theoret...
Preprint
Treatment response is heterogeneous. However the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. The artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized tre...
Preprint
Full-text available
As the Covid-19 pandemic soars around the world, there is urgent need to forecast the expected number of cases worldwide and the length of the pandemic before receding and implement public health interventions for significantly stopping the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory...
Preprint
Full-text available
The dynamical and structural aspects of cluster synchronization (CS) in complex systems have been intensively investigated in recent years. Here, we study CS of dynamical systems with intra and inter-cluster couplings. We propose new metrics that describe the performance of such systems and evaluate them as a function of the strength of the couplin...
Article
Full-text available
In this paper, we investigate the dynamics of population inhabiting a favorable environment surrounded by a region where survival is impossible. We use a parabolic partial differential equation with nonlinearity changing signs to describe the dynamics. By using a recent result on the existence of nonzero fixed point for r-nowhere normal-outward com...
Article
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Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation...
Preprint
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Objective: Develop the AI and casual inference-inspired methods for forecasting and evaluating the effects of public health interventions on curbing the spread of Covid-19. Methods: We developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public he...
Article
Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular ‘omics’ data, e.g. transcriptomics and proteomics, have been accumulated. The availability of these omics data...
Preprint
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Background The first case of COVID-19 atypical pneumonia was reported in Wuhan, China on December 1, 2019. Since then, at least 33 other countries have been affected and there is a possibility of a global outbreak. A tremendous amount of effort has been made to understand its transmission dynamics; however, the temporal and spatial transmission het...
Preprint
The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in Chi...
Article
The existence and nonexistence of semi-trivial or coexistence steady-state solutions of one-dimensional competition models in an unstirred chemostat are studied by establishing new results on systems of Hammerstein integral equations via the classical fixed point index theory. We provide three ranges for the two parameters involved in the competiti...
Article
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In the past several months, the widely spreading diseases caused by the novel coronavirus (COVID-19) have severely threatened the global public health security and global economics as well. Although the studies using the traditional compartmental epidemic models have rendered a series of profound and significant results on retrospect and prediction...
Article
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With the deregulation of modern power grids, electricity markets are playing a more and more important role in power grid operation and control. However, it is still questionable how the real-time electricity price-based operation affects power grid stability. From a complex network perspective, here we investigate the dynamical interactions betwee...
Article
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Waddington epigenetic landscape, as a classic metaphor, has been used to explain cellular development and differentiation. However, it remains challenging to quantify the epigenetic landscape. Especially, a key issue arises as what are the underlying mechanisms for the interplay between genetic and epigenetic regulations to govern cell fate decisio...
Article
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In this article, we focus on a topic of detecting unstable periodic orbits (UPOs) only based on the time series observed from the nonlinear dynamical system whose explicit model is completely unknown a priori. We articulate a data-driven and model-free method which connects a well-known machine learning technique, the reservoir computing, with a wi...
Article
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In this article, we report a phenomenon of collective dynamics on discrete-time complex networks: random temporal interaction matrix even of zero or/and small average are able to significantly enhance synchronization with probability one. According to current knowledge, there is no verifiably sufficient criterion on what kind of the random temporal...
Article
Synchronization is a phenomenon of the collective behavior of coupled oscillators and involves the detailed interplay of the intrinsic frequencies of the oscillators, the underlying topological features of their interaction network, and external perturbations. In this work we investigate, in the strong coupling regime, the response of a system to e...
Article
In this paper, we focus on the topic of stabilizing the Boolean control network (BCN) by an optimal event-triggered feedback control. By routinely transforming the BCN into its algebraic form, constructing the (reverse) weighted digraph and the hypergraph for the BCN, applying the shortest path algorithm to the hypergraph, we obtain an optimal even...
Article
In this article, we investigate the emergence of tissue dynamics with time delays of diffusion. Such emergent dynamics, describing the tissue homeostasis, usually correspond to particular tissue functions, which are attracting a tremendous amount of attention from both communities of mathematical modeling and systems biology. Specifically, in addit...
Article
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The interplay between time delay and phase shifts plays an important role for the regulation of synchronization and gives rise to interesting collective dynamical phenomena. Here we derive a stability criterion for the synchronous state in a system of identical oscillators in the presence of inertia, time delay, and a phase shift. We investigate ho...
Article
We generalize the main result on existence of nonzero nonnegative solutions of systems of second order elliptic boundary value problems obtained by Lan (2011). The motivation for the generalization is to propose and study the competition models of Ricker and Beverton–Holt types governed by such systems. To the best of our knowledge, there is little...
Article
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Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-...
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In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to susta...
Article
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Complex networked systems ranging from ecosystems and the climate to economic, social, and infrastructure systems can exhibit a tipping point (a "point of no return") at which a total collapse of the system occurs. To understand the dynamical mechanism of a tipping point and to predict its occurrence as a system parameter varies are of uttermost im...
Article
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In this paper, we investigate the almost sure stability of switched systems on randomly switching durations simultaneously with randomly switching interaction matrices. We not only allow the interaction matrix on each switching duration to take values randomly from either a countable, an uncountable, or even an unbounded state space, but also allow...
Article
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We present here an adaptive control scheme with a feedback delay to achieve elimination of synchronization in a large population of coupled and synchronized oscillators. We validate the feasibility of this scheme not only in the coupled Kuramoto’s oscillators with a unimodal or bimodal distribution of natural frequency, but also in two representati...