We have further investigated Turing patterns in a reaction-diffusion system by theoretical analysis and numerical simulations. Simple Turing patterns and complex superlattice structures are observed. We find that the ...We have further investigated Turing patterns in a reaction-diffusion system by theoretical analysis and numerical simulations. Simple Turing patterns and complex superlattice structures are observed. We find that the shape and type of Turing patterns depend on dynamical parameters and external periodic forcing, and is independent of effective diffusivity rate σ in the Lengyel Epstein model Our numerical results provide additional insight into understanding the mechanism of development of Turing patterns and predicting new pattern formations.展开更多
The study of rumor propagation dynamics is of great significance to reduce.false news and ensure the authenticity of news information.In this paper,a SI reaction-diffusion rumor propagation model with nonlinear satura...The study of rumor propagation dynamics is of great significance to reduce.false news and ensure the authenticity of news information.In this paper,a SI reaction-diffusion rumor propagation model with nonlinear saturation incidence is studied.First,through stability analysis,we obtain the conditions for the existence and local stability of the positive equilibrium point.By selecting suitable variable as the control parameter,the critical value of Turing bifurcation and the existence theorem of Turing bifurcation are obtained.Then,using the above theorem and multi-scale standard analysis,the expression of amplitude equation around Turing bifurcation point is obtained.By analyzing the amplitude equation,different types of Turing pattern are divided such as uniform steady-state mode,hexagonal mode,stripe mode and mixed structure mode.Further,in the numerical simulation part,by observing different patterns corresponding to different values of control variable,the correctness of the theory is verified.Finally,the effects of different network structures on patterns are investigated.The results show that there are significant differences in the distribution of users on different network structures.展开更多
In this paper, a strongly coupled diffusive predator-prey system with a modified Leslie- Gower term is considered. We will show that under certain hypotheses, even though the unique positive equilibrium is asymptotica...In this paper, a strongly coupled diffusive predator-prey system with a modified Leslie- Gower term is considered. We will show that under certain hypotheses, even though the unique positive equilibrium is asymptotically stable for the dynamics with diffusion, Turing instability can produce due to the presence of the cross-diffusion. In particular, we establish the existence of non-constant positive steady states of this system. The results indicate that cross-diffusion can create stationary patterns.展开更多
In this paper, we consider a sex-structured predator prey model with strongly coupled nonlinear reaction diffusion. Using the Lyapunov functional and Leray Schauder degree theory, the existence and stability of both h...In this paper, we consider a sex-structured predator prey model with strongly coupled nonlinear reaction diffusion. Using the Lyapunov functional and Leray Schauder degree theory, the existence and stability of both homogenous and heterogenous steady-states are investigated. Our results demonstrate that the unique homogenous steady-state is locally asymptotically stable for the associated ODE system and PDE system with self-diffusion. With the presence of the cross-diffusion, the homogeneous equilibrium is destabilized, and a heterogenous steady-state emerges as a consequence. In addition, the conditions guaranteeing the emergence of Turing patterns are derived.展开更多
The formation of spatial patterns is an important issue in reaction–diffusion systems.Previous studies have mainly focused on the spatial patterns in reaction–diffusion models equipped with symmetric diffusion(such ...The formation of spatial patterns is an important issue in reaction–diffusion systems.Previous studies have mainly focused on the spatial patterns in reaction–diffusion models equipped with symmetric diffusion(such as normal or fractional Laplace diffusion),namely,assuming that spatial environments of the systems are homogeneous.However,the complexity and heterogeneity of spatial environments of biochemical reactions in vivo can lead to asymmetric diffusion of reactants.Naturally,there arises an open question of how the asymmetric diffusion affects dynamical behaviors of biochemical reaction systems.To answer this,we build a general asymmetric L´evy diffusion model based on the theory of a continuous time random walk.In addition,we investigate the two-species Brusselator model with asymmetric L´evy diffusion,and obtain a general condition for the formation of Turing and wave patterns.More interestingly,we find that even though the Brusselator model with symmetric diffusion cannot produce steady spatial patterns for some parameters,the asymmetry of L´evy diffusion for this model can produce wave patterns.This is different from the previous result that wave instability requires at least a three-species model.In addition,the asymmetry of L´evy diffusion can significantly affect the amplitude and frequency of the spatial patterns.Our results enrich our knowledge of the mechanisms of pattern formation.展开更多
Traditionally,the spatiotemporal dynamics of neural networks have been analyzed in the locally continuous domain.While this modeling approach is straightforward and practical,it fails to encapsulate real-world network...Traditionally,the spatiotemporal dynamics of neural networks have been analyzed in the locally continuous domain.While this modeling approach is straightforward and practical,it fails to encapsulate real-world networks’intricate topological structures and evolution.Currently,the mechanisms of stability switches induced by time delays and diffusion effects in networkorganized systems are still vague.Besides,effective dynamic optimization strategies for network-organized models remain to be devised.In this study,we develop a delayed reaction-diffusion neural network model on complex networks.This system incorporates the effects of inter-nodal diffusion coupling and exhibits profound architectural complexity.We then pioneer the proportional-integral-derivative(PID)feedback control into the network-organized model to modulate dynamic behaviors.The linear stability analysis is conducted firstly,demonstrating that Turing patterns and Hopf bifurcation can be induced in the controlled neural network by varying diffusion coefficients and time delays.Subsequently,the bifurcation direction is deduced via the center manifold theorem.Finally,a series of simulations are performed to validate the theoretical analysis and substantiate the efficacy of the PID control strategy.The results exhibit that the PID feedback controller can flexibly regulate the dynamics of the network-organized systems and possesses excellent disturbance rejection capabilities.展开更多
A lattice Boltzmann model for the study of advection-diffusion-reaction(ADR)problems is proposed.Via multiscale expansion analysis,we derive from the LB model the resulting macroscopic equations.It is shown that a lin...A lattice Boltzmann model for the study of advection-diffusion-reaction(ADR)problems is proposed.Via multiscale expansion analysis,we derive from the LB model the resulting macroscopic equations.It is shown that a linear equilibrium distribution is sufficient to produce ADR equations within error terms of the order of the Mach number squared.Furthermore,we study spatially varying structures arising from the interaction of advective transport with a cubic autocatalytic reaction-diffusion process under an imposed uniform flow.While advecting all the present species leads to trivial translation of the Turing patterns,differential advection leads to flow induced instability characterized with traveling stripes with a velocity dependent wave vector parallel to the flow direction.Predictions from a linear stability analysis of the model equations are found to be in line with these observations.展开更多
基金The project supported by National Natural Science Foundation of China under Grant No. 10374089 and the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant No. KJCX2-SW-W17
文摘We have further investigated Turing patterns in a reaction-diffusion system by theoretical analysis and numerical simulations. Simple Turing patterns and complex superlattice structures are observed. We find that the shape and type of Turing patterns depend on dynamical parameters and external periodic forcing, and is independent of effective diffusivity rate σ in the Lengyel Epstein model Our numerical results provide additional insight into understanding the mechanism of development of Turing patterns and predicting new pattern formations.
基金supported by the National Natural Science Foundation of China(Grant No.12002135)Young Science and Technology Talents Lifting Project of Jiangsu Association for Science and Technology.
文摘The study of rumor propagation dynamics is of great significance to reduce.false news and ensure the authenticity of news information.In this paper,a SI reaction-diffusion rumor propagation model with nonlinear saturation incidence is studied.First,through stability analysis,we obtain the conditions for the existence and local stability of the positive equilibrium point.By selecting suitable variable as the control parameter,the critical value of Turing bifurcation and the existence theorem of Turing bifurcation are obtained.Then,using the above theorem and multi-scale standard analysis,the expression of amplitude equation around Turing bifurcation point is obtained.By analyzing the amplitude equation,different types of Turing pattern are divided such as uniform steady-state mode,hexagonal mode,stripe mode and mixed structure mode.Further,in the numerical simulation part,by observing different patterns corresponding to different values of control variable,the correctness of the theory is verified.Finally,the effects of different network structures on patterns are investigated.The results show that there are significant differences in the distribution of users on different network structures.
文摘In this paper, a strongly coupled diffusive predator-prey system with a modified Leslie- Gower term is considered. We will show that under certain hypotheses, even though the unique positive equilibrium is asymptotically stable for the dynamics with diffusion, Turing instability can produce due to the presence of the cross-diffusion. In particular, we establish the existence of non-constant positive steady states of this system. The results indicate that cross-diffusion can create stationary patterns.
文摘In this paper, we consider a sex-structured predator prey model with strongly coupled nonlinear reaction diffusion. Using the Lyapunov functional and Leray Schauder degree theory, the existence and stability of both homogenous and heterogenous steady-states are investigated. Our results demonstrate that the unique homogenous steady-state is locally asymptotically stable for the associated ODE system and PDE system with self-diffusion. With the presence of the cross-diffusion, the homogeneous equilibrium is destabilized, and a heterogenous steady-state emerges as a consequence. In addition, the conditions guaranteeing the emergence of Turing patterns are derived.
基金supported by the National Natural Science Foundation of China(Grant Nos.62066026,62363027,and 12071408)PhD program of Entrepreneurship and Innovation of Jiangsu Province,Jiangsu University’Blue Project’,the Natural Science Foundation of Jiangxi Province(Grant No.20224BAB202026)the Science and Technology Research Project of Jiangxi Provincial Department of Education(Grant No.GJJ2203316).
文摘The formation of spatial patterns is an important issue in reaction–diffusion systems.Previous studies have mainly focused on the spatial patterns in reaction–diffusion models equipped with symmetric diffusion(such as normal or fractional Laplace diffusion),namely,assuming that spatial environments of the systems are homogeneous.However,the complexity and heterogeneity of spatial environments of biochemical reactions in vivo can lead to asymmetric diffusion of reactants.Naturally,there arises an open question of how the asymmetric diffusion affects dynamical behaviors of biochemical reaction systems.To answer this,we build a general asymmetric L´evy diffusion model based on the theory of a continuous time random walk.In addition,we investigate the two-species Brusselator model with asymmetric L´evy diffusion,and obtain a general condition for the formation of Turing and wave patterns.More interestingly,we find that even though the Brusselator model with symmetric diffusion cannot produce steady spatial patterns for some parameters,the asymmetry of L´evy diffusion for this model can produce wave patterns.This is different from the previous result that wave instability requires at least a three-species model.In addition,the asymmetry of L´evy diffusion can significantly affect the amplitude and frequency of the spatial patterns.Our results enrich our knowledge of the mechanisms of pattern formation.
基金supported by the National Natural Science Foundation of China(Grant Nos.62073172,62233004,62073076)the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20221329)Jiangsu Provincial Scientific Research Center of Applied Mathematics(Grant No.BK20233002).
文摘Traditionally,the spatiotemporal dynamics of neural networks have been analyzed in the locally continuous domain.While this modeling approach is straightforward and practical,it fails to encapsulate real-world networks’intricate topological structures and evolution.Currently,the mechanisms of stability switches induced by time delays and diffusion effects in networkorganized systems are still vague.Besides,effective dynamic optimization strategies for network-organized models remain to be devised.In this study,we develop a delayed reaction-diffusion neural network model on complex networks.This system incorporates the effects of inter-nodal diffusion coupling and exhibits profound architectural complexity.We then pioneer the proportional-integral-derivative(PID)feedback control into the network-organized model to modulate dynamic behaviors.The linear stability analysis is conducted firstly,demonstrating that Turing patterns and Hopf bifurcation can be induced in the controlled neural network by varying diffusion coefficients and time delays.Subsequently,the bifurcation direction is deduced via the center manifold theorem.Finally,a series of simulations are performed to validate the theoretical analysis and substantiate the efficacy of the PID control strategy.The results exhibit that the PID feedback controller can flexibly regulate the dynamics of the network-organized systems and possesses excellent disturbance rejection capabilities.
基金supported by the Max-Planck-Institut fur Eisenforschungby the Interdisciplinary Centre for Advanced Material Simulation(ICAMS),Ruhr Universitat Bochum.
文摘A lattice Boltzmann model for the study of advection-diffusion-reaction(ADR)problems is proposed.Via multiscale expansion analysis,we derive from the LB model the resulting macroscopic equations.It is shown that a linear equilibrium distribution is sufficient to produce ADR equations within error terms of the order of the Mach number squared.Furthermore,we study spatially varying structures arising from the interaction of advective transport with a cubic autocatalytic reaction-diffusion process under an imposed uniform flow.While advecting all the present species leads to trivial translation of the Turing patterns,differential advection leads to flow induced instability characterized with traveling stripes with a velocity dependent wave vector parallel to the flow direction.Predictions from a linear stability analysis of the model equations are found to be in line with these observations.
