• The diffusion-driven instability and complexity for a single-handed discrete Fisher equation

      Yan, Yubin; Zhang, Guang; Zhang, Ruixuan; University of Chester; Tianjin University of Commerce (Elsevier, 2019-12-19)
      For a reaction diffusion system, it is well known that the diffusion coefficient of the inhibitor must be bigger than that of the activator when the Turing instability is considered. However, the diffusion-driven instability/Turing instability for a single-handed discrete Fisher equation with the Neumann boundary conditions may occur and a series of 2-periodic patterns have been observed. Motivated by these pattern formations, the existence of 2-periodic solutions is established. Naturally, the periodic double and the chaos phenomenon should be considered. To this end, a simplest two elements system will be further discussed, the flip bifurcation theorem will be obtained by computing the center manifold, and the bifurcation diagrams will be simulated by using the shooting method. It proves that the Turing instability and the complexity of dynamical behaviors can be completely driven by the diffusion term. Additionally, those effective methods of numerical simulations are valid for experiments of other patterns, thus, are also beneficial for some application scientists.
    • Multi-order fractional differential equations and their numerical solution

      Diethelm, Kai; Ford, Neville J.; Technische Universität Braunschweig ; University College Chester (Elsevier, 2004-07-15)
      This article considers the numerical solution of (possibly nonlinear) fractional differential equations of the form y(α)(t)=f(t,y(t),y(β1)(t),y(β2)(t),…,y(βn)(t)) with α>βn>βn−1>>β1 and α−βn1, βj−βj−11, 0
    • Numerical investigation of noise induced changes to the solution behaviour of the discrete FitzHugh-Nagumo equation

      Ford, Neville J.; Lima, Pedro M.; Lumb, Patricia M.; University of Chester, Instituto Superior Tecnico, University of Lisbon, University of Chester (Elsevier, 2016-09-08)
      In this work we introduce and analyse a stochastic functional equation, which contains both delayed and advanced arguments. This equation results from adding a stochastic term to the discrete FitzHugh-Nagumo equation which arises in mathematical models of nerve conduction. A numerical method is introduced to compute approximate solutions and some numerical experiments are carried out to investigate their dynamical behaviour and compare them with the solutions of the corresponding deterministic equation.