Reaction-diffusion Partial Differential Equations Research Articles

An Efficient Technique to Study Time Fractional Extended Fisher-Kolmogorov Equation

Reaction-diffusion partial differential equations are among the most widely used equations in applied mathematical modelling. In this study, we examine the solutions of one such equation, namely, the time-fractional extended Fisher-Kolmogorov equation. This equation is widely used in the study of population growth and wave propagation dynamics. The technique we consider is a combination of the natural transform and the Adomian decomposition method. Fractional derivatives are considered with singular and non-singular kernels. The existence and uniqueness of the solutions are presented. We analyse two different cases of the proposed problem to determine the validity and efficacy of the proposed scheme. Additionally, numerical simulation is shown, and the nature of the achieved solution is captured in terms of plots for various fractional orders. The outcome demonstrates that the method is straightforward, efficient, and dependable. The proposed method does not require any predetermined assumptions, linearization, perturbation, or discretization, and it prevents rounding errors. Therefore, the technique is ready to be implemented for a variety of nonlinear time fractional partial differential equations.

A model about regulation on three division modes of stem cell

We construct a multi-stage cell lineage model for cell division, apoptosis and movement. Cells are assumed to secrete and respond to negative feedback molecules which act as a control on the stem cell divisions (including self-renewal, asymmetrical cell division (ACD) and differentiation). The densities of cells and molecules are described by coupled reaction–diffusion partial differential equations, and the plane wavefront propagation speeds can be obtained analytically and verified numerically. It is found that with ACD the population and propagation of stem cells can be promoted but the negative regulation on self-renewal and differentiation will work slowly. Regulatory inhibition on differentiation will inversely increase stem cells but not affect the population and wave propagation of the cell lineage. While negative regulation on self-renewal and ACD will decrease the population of stem cells and slow down the propagation, and even drive stem cells to extinction. Moreover we find that inhibition on self-renewal has a strength advantage while inhibition on ACD has a range advantage to kill stem cells. Possible relations to model cancer development and therapy are also discussed.

Multi-agent deployment in 3-D via reaction–diffusion system with radially-varying reaction

This paper considers the problem of the deployment of a set of agents distributed on a disk-shaped grid onto three-dimensional (3-D) profiles, by using a continuum approximation (valid in the limit for a large number of agents) and then a control methodology for partial differential equations (PDEs). The agents’ collective behavior is modeled by a pair of radially-varying diffusion–reaction PDEs in polar coordinates, whose state determines the agents’ position. Having a radially-varying reaction coefficient not only increases the challenge of kernel equations becoming singular in radius, but also brings more potential deployment manifolds. To stabilize and increase the convergence of the deployment, a boundary controller and a boundary observer are designed by combining an infinite-dimensional backstepping approach with a Fourier series decomposition technique, thus driving all agents to the desired profile. A key feature of the presented result is that the desired profile only needs to be known by the leaders, with the followers only needing to follow a simple control strategy which requires only the measurement of its current position and communication with its neighbors as defined by the multi-agent system topology. The method provides closed-loop exponential stability with any prescribed convergence rate in the L2 norm. Simulation tests are shown to prove the effectiveness of the proposed algorithm.

Modelling count data with partial differential equation models in biology

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth–death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction–diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Adaptive output feedback event-triggered consensus control for networked reaction-diffusion PDE systems

This paper explores the adaptive output feedback event-triggered consensus control of a network of parabolic systems modelled by partial differential equations (PDEs). An infinite-dimensional state observer is established to estimate the state based on the relative output information. On the basis of the adaptive state observer, two novel distributed event-triggered controllers are proposed to address the leaderless consensus and leader-follower consensus problem. Based on the Lyapunov method and PDE theory, it is shown that the asymptotic consensus control can be achieved. Compared to the related works, a distinctive feature of the designed event-triggered consensus protocols is fully distributed which does not demand any global information of the graph. In addition, the absence of Zeno behaviour is demonstrated. Lastly, the obtained results are demonstrated using two simulations.

Pulse-adding of temporal dissipative solitons: resonant homoclinic points and the orbit flip of case B with delay

We numerically investigate the branching of temporally localised, two-pulse solutions from one-pulse periodic solutions with non-oscillating tails in delay differential equations (DDEs) with large delay. Solutions of this type are commonly referred to as temporal dissipative solitons (TDSs) (Yanchuk et al 2019 Phys. Rev. Lett. 123 53901) in applications, and we adopt this term here. We show by means of a prototypical example that—analogous to travelling pulses in reaction–diffusion partial differential equations (Yanagida 1987 J. Differ. Equ. 66 243–62)—the branching of two-pulse TDSs from one-pulse TDSs with non-oscillating tails is organised by codimension-two homoclinic bifurcation points of a real saddle equilibrium (Homburg and Sandstede 2010 Handbook of Dynamical Systems Elsevier) in a corresponding profile equation. We consider a generalisation of Sandstede’s model (Sandstede 1997 J. Dyn. Differ. Equ. 9 269–88) (a prototypical model for studying codimension-two homoclinic bifurcation points in ordinary differential equations) with an additional time-shift parameter, and use Auto07p (Doedel 1981 Congr. Numer. 30 265–84; Doedel and Oldeman 2010 AUTO-07P: Continuation and Bifurcation Software for Ordinary Differential Equations Concordia University) and DDE-BIFTOOL (Sieber et al 2014 arXiv:1406.7144) to compute numerically the unfolding of these bifurcation points in the resulting DDE. We then interpret this model as the profile equation for TDSs in a DDE with large delay by exploiting the reappearance of periodic solutions in DDEs (Yanchuk and Perlikowski 2009 Phys. Rev. E 79 046221). In doing so, we identify both the non-orientable resonant homoclinic bifurcation and the orbit flip bifurcation of case B as organising centres for the existence of two-pulse TDSs in the DDE with large delay. We study the bifurcation curves emanating from these codimension-two points beyond a local neighbourhood in parameter space. In this way, we are able to discuss how folds of homoclinic bifurcations in an extended system bound the existence region of TDSs in the DDE with large delay. We also discuss the relation between a reduced multivalued-map (in the limit of infinite delay) and the existence of TDSs.

Effect of cellular dedifferentiation on the growth of cell lineages

We construct a cell lineage model with coupled reaction-diffusion partial differential equations. The population dynamics can be obtained analytically and verified numerically. With cellular dedifferentiation it will change the number of homogeneous steady states, unify the wavefront profiles and promote propagating speeds of cell lineage. Especially in three-stage cell lineage, with jumpwise dedifferentiation the population and propagation speeds of cell lineage depend on the product of all cells’ differentiation/dedifferentiation rates; with stepwise dedifferentiation the population and propagation speeds depend on the intra-group product and inter-group product of all cells’ differentiation/dedifferentiation rates respectively. Furthermore in redistributing the cell lineage population more stem cells can be produced by jumpwise dedifferentiation than by stepwise dedifferentiation. The propagation of cell lineages can be promoted by stepwise dedifferentiation with fast movement of middle cells or jumpwise dedifferentiation with high death rate of middle cells though the proportion of stem cells is different. Possible relations to cancer development and therapy will be discussed.

Computing all persistent subspaces of a reaction-diffusion system

An algorithm is presented for computing a reaction-diffusion partial differential equation (PDE) system for all possible subspaces that can hold a persistent solution of the equation. For this, all possible sub-networks of the underlying reaction network that are distributed organizations (DOs) are identified. Recently it has been shown that a persistent subspace must be a DO. The algorithm computes the hierarchy of DOs starting from the largest by a linear programming approach using integer cuts. The underlying constraints use elementary reaction closures as minimal building blocks to guarantee local closedness and global self-maintenance, required for a DO. Additionally, the algorithm delivers for each subspace an affiliated set of organizational reactions and minimal compartmentalization that is necessary for this subspace to persist. It is proved that all sets of organizational reactions of a reaction network, as already DOs, form a lattice. This lattice contains all potentially persistent sets of reactions of all constrained solutions of reaction-diffusion PDEs. This provides a hierarchical structure of all persistent subspaces with regard to the species and also to the reactions of the reaction-diffusion PDE system. Here, the algorithm is described and the corresponding Python source code is provided. Furthermore, an analysis of its run time is performed and all models from the BioModels database as well as further examples are examined. Apart from the practical implications of the algorithm the results also give insights into the complexity of solving reaction-diffusion PDEs.

Event-triggered boundary consensus control for multi-agent systems of fractional reaction–diffusion PDEs

The distributed consensus is considered for multi-agent systems (MASs), which characterized by fractional reaction–diffusion partial differential equations (RDPDEs) in this paper. Based on Lyapunov technique and linear matrix inequalities (LMIs) theory, the consensus can be realized via two novel event-triggered boundary control schemes. Firstly, a novel convergence principle subject to finite time is presented for the continuously differentiable function. Secondly, the cooling fin on surface of high-speed aerospace vehicle is remodeled by fractional RDPDEs system, and the well-posedness of presented system is discussed applying the monotone iterative approach. Thirdly, according to the presented static event-triggered boundary control strategy, the consensus criterion in finite time is addressed in the form of LMIs, in addition, the settling time is calculated accurately. Applying the dynamic event-triggered control protocol, the Mittag-Leffler (M-L) consensus condition is achieved. Moreover, the Zeno behaviors are ruled out for proposed event-triggered mechanisms. Finally, the high-speed aerospace vehicle model is presented to verify the effectiveness of the control performance.

Bilateral boundary control of an input delayed 2-D reaction–diffusion equation

In this paper, a delay compensation design method based on PDE backstepping is developed for a two-dimensional reaction–diffusion partial differential equation (PDE) with bilateral input delays. The PDE is defined in a rectangular domain, and the bilateral control is imposed on a pair of opposite sides of the rectangle. To represent the delayed bilateral inputs, we introduce two 2-D transport PDEs that form a cascade system with the original PDE. A novel set of backstepping transformations is proposed for delay compensator design, including one Volterra integral transformation and two affine Volterra integral transformations. Unlike the kernel equation for 1-D PDE systems with delayed boundary input, the resulting kernel equations for the 2-D system have singular initial conditions governed by the Dirac Delta function. Consequently, the kernel solutions are written as a double trigonometric series with singularities. To address the challenge of stability analysis posed by the singularities, we prove a set of inequalities by using the Cauchy–Schwarz inequality, the 2-D Fourier series, and the Parseval’s theorem. A numerical simulation illustrates the effectiveness of the proposed delay-compensation method.

An open-source pipeline for solving continuous reaction–diffusion models in image-based geometries of porous media

We present a versatile open-source pipeline for simulating inhomogeneous reaction–diffusion processes in highly resolved, image-based geometries of porous media with reactive boundaries. Resolving realistic pore-scale geometries in numerical models is challenging and computationally demanding, as the scale differences between the sizes of the interstitia and the whole system can lead to prohibitive memory requirements. The present pipeline combines a level-set method with geometry-adapted sparse block grids on GPUs to efficiently simulate reaction–diffusion processes in image-based geometries. We showcase the method by applying it to fertilizer diffusion in soil, heat transfer in porous ceramics, and determining effective diffusion coefficients and tortuosity. The present approach enables solving reaction–diffusion partial differential equations in real-world geometries applicable to porous media across fields such as engineering, environmental science, and biology.

Bifurcations and Early-Warning Signs for SPDEs with Spatial Heterogeneity

AbstractBistability is a key property of many systems arising in the nonlinear sciences. For example, it appears in many partial differential equations (PDEs). For scalar bistable reaction-diffusions PDEs, the bistable case even has taken on different names within communities such as Allee, Allen-Cahn, Chafee-Infante, Nagumo, Ginzburg-Landau,$$\Phi ^4$$Φ4, Schlögl, Stommel, just to name a few structurally similar bistable model names. One key mechanism, how bistability arises under parameter variation is a pitchfork bifurcation. In particular, taking the pitchfork bifurcation normal form for reaction-diffusion PDEs is yet another variant within the family of PDEs mentioned above. More generally, the study of this PDE class considering steady states and stability, related to bifurcations due to a parameter is well-understood for the deterministic case. For the stochastic PDE (SPDE) case, the situation is less well-understood and has been studied recently. In this paper we generalize and unify several recent results for SPDE bifurcations. Our generalisation is motivated directly by applications as we introduce in the equation a spatially heterogeneous term and relax the assumptions on the covariance operator that defines the noise. For this spatially heterogeneous SPDE, we prove a finite-time Lyapunov exponent bifurcation result. Furthermore, we extend the theory of early warning signs in our context and we explain, the role of universal exponents between covariance operator warning signs and the lack of finite-time Lyapunov uniformity. Our results are accompanied and cross-validated by numerical simulations.

Modeling of Bacterial Communication in the Extended Range of Population Dynamics

”Quorum sensing” as a special kind of communication in bacterial populations can be analyzed by means of methods and techniques of mathematical modeling and computer simulation. In the present study, a modification of a deterministic mathematical model of bacterial quorum sensing is proposed, taking into account the law of multiphase population dynamics. The mathematical model is formalized by an initial-boundary value problem for a system of semilinear reaction-diffusion partial differential equations. The equations include generation terms in view of changes in the biomass density. The model describes space-time dynamics of concentrations of special substances (signaling agents and Lactonase enzymes) that characterize the quorum sensing in Gram-negative bacteria. The problem is solved by means of the finite element method using the COMSOL Multiphysics platform. Computational experiments are performed to estimate concentrations of key substances characterizing quorum sensing for Pseudomonas putida bacterial strains in an expanded range of population dynamics.

A cell model about symmetric and asymmetric stem cell division

We construct a multi-stage cell lineage model including self-renewal, apoptosis, cell movement and the symmetrical/asymmetrical division of stem cells. The evolution of cell populations can be described by coupled reaction–diffusion partial differential equations, and the propagating wavefront speeds can be obtained analytically and verified by numerical solutions of the equations. The emphasis is on the effect of symmetric/asymmetric division of stem cells on the population and propagating dynamics of cell lineage. It is found that stem cells’ asymmetric cell division (ACD) can move the phase boundary of the homogenous solution of the system. The population of the cell lineage will be promoted in presence of ACD. The concentration of stem cells increases with ACD but that of differentiated daughter cells decreases with ACD. In addition, it is found that the propagating speed of the stem cells can be evaluated with ACD. When the daughter cells move fast to a new space, stem cells can catch them up through increasing ACD. Our results may suggest a mechanism of collective migration of cell lineage through cooperation between ACD of stem cells and fast diffusion of the daughter cells.

A 3D Haar wavelet method for a coupled degenerate system of parabolic equations with nonlinear source coupled with non-linear ODEs

This study presents a robust and easy-to-implement stable numerical method based on Haar wavelets for the coupled degenerate reaction-diffusion partial differential equations having a nonlinear source with homogeneous Neumann boundary data and interacting with a system of nonlinear ODEs. The simple requirement of one-time computation of the associated Haar matrices and the Haar integral matrices during the entire calculation of the numerical solution makes the proposed method both cost-effective and simple. By projecting the higher-order derivatives into the Haar wavelet space the method effectively uses a discontinuous basis for solving the degenerate system. The Lax equivalence criterion is followed to prove the convergence of the proposed Haar wavelet method. The method is exponentially convergent and is directly extendable to the higher dimensions. All the quadratures in the proposed method are done symbolically without any approximation error. Multidimensional numerical examples, including a few models of medical significance dealing with cardiac electrical activity, are considered to illustrate the good performance of the proposed method. It is shown that solving the degenerate model equations using the proposed method is easy to implement and cost-effective while moving to higher dimensions in comparison to the finite element method. The problem with discontinuity in the parameter of the model, which is relevant to the case solving electrical activity problem in the presence of ischemia in cardiac tissue, is also solved using the proposed method.

A robust nonlinear model of steam reformers with applications to energy efficiency

We have developed and implemented a computational methodology to solve for the heat flow and the gas concentrations in a class of industrial catalytic steam reformers. The problem under consideration consists of a nonlinearly coupled system composed of a boundary-value problem and reaction-diffusion partial differential equations. Such reformers have broad industrial applications and consume a large amount of energy and natural resources globally. The methodology is applied to tackle two outstanding questions regarding catalytic processes, for which our methodology delivers efficient answers to the following research questions: (i) How to solve the energy/catalyst investment dichotomy, i.e., locations with relatively inexpensive energy require less catalyst and in the opposite case of high energy costs, more catalyst is needed. (ii) How to express the energy input, which is difficult to ascertain for large burner designs, in terms of easily measurable parameters in real time, such as the input temperatures. These questions are answered by our approach leading to a powerful design tool for catalytic processes in general, with huge potential to mitigate environmental impacts. The complex nonlinear system of reaction-diffusion equations is solved by means of an implicit-explicit method and the nonlinear boundary value problem is solved by an iterative method. The coupling between the two systems is also performed through an iterative method. The convergence of the latter is confirmed by extensive numerical investigations, which are consistent with observed measurements in industrial installations. Furthermore, an analysis of the syngas production with different catalyst density, reactor inlet temperature, furnace energy flame and catalyst length are performed, which confirm that the variation of these parameters can result in a more optimized process. A typical consequence is the following: Fixing all parameters, except the catalyst density and the reactor inlet temperature, we show that an increase of 100 K in the latter allows us to use 3 times less catalyst density to achieve the same target CO concentration. In other words, we have a quantifiable and substantial trade off between energy and catalyst investment.

Scalable and Robust Dual-Primal Newton–Krylov Deluxe Solvers for Cardiac Electrophysiology with Biophysical Ionic Models

The focus of this work is to provide an extensive numerical study of the parallel efficiency and robustness of a staggered dual-primal Newton–Krylov deluxe solver for implicit time discretizations of the Bidomain model. This model describes the propagation of the electrical impulse in the cardiac tissue, by means of a system of parabolic reaction-diffusion partial differential equations. This system is coupled to a system of ordinary differential equations, modeling the ionic currents dynamics. A staggered approach is employed for the solution of a fully implicit time discretization of the problem, where the two systems are solved successively. The arising nonlinear algebraic system is solved with a Newton–Krylov approach, preconditioned by a dual-primal Domain Decomposition algorithm in order to improve convergence. The theoretical analysis and numerical validation of this strategy has been carried out in Huynh et al. (SIAM J. Sci. Comput. 44, B224–B249, 2022) considering only simple ionic models. This paper extends this study to include more complex biophysical ionic models, as well as the presence of ischemic regions, described mathematically by heterogeneous diffusion coefficients with possible discontinuities between subregions. The results of several numerical experiments show robustness and scalability of the proposed parallel solver.

Modeling nonlocal behavior in epidemics via a reaction–diffusion system incorporating population movement along a network

The outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works have focused on partial differential equation (PDE) models, particularly reaction–diffusion models, able to describe the progression of an epidemic in both space and time. These studies have shown generally promising results in describing and predicting COVID-19 progression. However, people often travel long distances in short periods of time, leading to nonlocal transmission of the disease. Such contagion dynamics are not well-represented by diffusion alone. In contrast, ordinary differential equation (ODE) models may easily account for this behavior by considering disparate regions as nodes in a network, with the edges defining nonlocal transmission. In this work, we attempt to combine these modeling paradigms via the introduction of a network structure within a reaction–diffusion PDE system. This is achieved through the definition of a population-transfer operator, which couples disjoint and potentially distant geographic regions, facilitating nonlocal population movement between them. We provide analytical results demonstrating that this operator does not disrupt the physical consistency or mathematical well-posedness of the system, and verify these results through numerical experiments. We then use this technique to simulate the COVID-19 epidemic in the Brazilian region of Rio de Janeiro, showcasing its ability to capture important nonlocal behaviors, while maintaining the advantages of a reaction–diffusion model for describing local dynamics.

Nonstandard finite differences numerical methods for a vegetation reaction–diffusion model

In this work we derive NonStandard Finite Differences (NSFDs) (Anguelov and Lubuma, 2001; Mickens, 2020) numerical schemes to solve a model consisting of reaction–diffusion Partial Differential Equations (PDEs) that describes the coexistence of plant species in arid environments (Eigentler and Sherratt, 2019). The new methods are constructed by exploiting a-priori known properties of the exact solution, such as positivity and oscillating behavior in space. Furthermore, we extend the definition of NSFDs inspired by the Time-Accurate and High-Stable Explicit (TASE) (Bassenne et al., 2021) methodology, also exploring the existing connections between nonstandard methods and the Exponential-Fitting (EF) (Ixaru, 1997; Ixaru and Berghe, 2010) technique.Several numerical tests are performed to highlight the best properties of the new NSFDs methods compared to the related standard ones. In fact, at the same cost, the former are much more stable than the latter, and unlike them preserve all the most important features of the model.

Integral equation-based simulation of transient experiments for an [formula omitted] mechanism: Beyond the steady state simplification

Transient electrochemical experiments are usually described theoretically by systems of reaction–diffusion partial differential equations. Converting them to integral equations is a classical and valuable modelling approach. Unfortunately, if any reaction–diffusion equation contains nonlinear reaction rate terms, up to now such a conversion has only been possible by assuming a steady state for the equation. Consequently, only sufficiently fast homogeneous reactions could be handled. In this work a novel integral equation-based modelling approach is described. The steady state assumption is replaced by a two-term singular perturbation expansion of the concentration–flux relationship, recently published by the authors. The expansion is valid for homogeneous reactions of (integer) order m≥1, occurring at planar electrodes. An example simulation of cyclic voltammetry for an EC2 reaction mechanism involving a second order dimerization reaction is performed. It is found that in this way the voltammograms can be satisfactorily simulated for homogeneous reaction rate constants smaller by about one order of magnitude than was previously possible.