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We formulate an immuno-epidemiological model of coupled “within-host” model of ODEs and“between-host” model of ODE and PDE, using the Human Immunodeficiency Virus (HIV) forillustration. Existence and uniqueness of solution to the “between-host” model isestablished, and an explicit expression for the basic reproduction number of the“between-host” model derived. Stability of disease-free and endemic equilibria isinvestigated. An optimal control problem with drug-treatment control on the within-hostsystem is formulated and analyzed; these results are novel for optimal control of ODEslinked with such first order PDEs. Numerical simulations based on the forward-backwardsweep method are obtained.
This paper investigates the output controllability problem of temporal Boolean networkswith inputs (control nodes) and outputs (controlled nodes). A temporal Boolean network isa logical dynamic system describing cellular networks with time delays. Using semi-tensorproduct of matrices, the temporal Boolean networks can be converted into discrete timelinear dynamic systems. Some necessary and sufficient conditions on the outputcontrollability via two kinds of inputs are obtained by providingcorresponding reachable sets. Two examples are given to illustrate the obtainedresults.
We consider a damped abstract second order evolution equation with an additionalvanishing damping of Kelvin–Voigt type. Unlike the earlier work by Zuazua and Ervedoza, wedo not assume the operator defining the main damping to be bounded. First, using aconstructive frequency domain method coupled with a decomposition of frequencies and theintroduction of a new variable, we show that if the limit system is exponentially stable,then this evolutionary system is uniformly − with respect to the calibration parameter −exponentially stable. Afterwards, we prove uniform polynomial and logarithmic decayestimates of the underlying semigroup provided such decay estimates hold for the limitsystem. Finally, we discuss some applications of our results; in particular, the case ofboundary damping mechanisms is accounted for, which was not possible in the earlier workmentioned above.
Galerkin reduced-order models for the semi-discrete wave equation, that preserve thesecond-order structure, are studied. Error bounds for the full state variables are derivedin the continuous setting (when the whole trajectory is known) and in the discrete settingwhen the Newmark average-acceleration scheme is used on the second-order semi-discreteequation. When the approximating subspace is constructed using the proper orthogonaldecomposition, the error estimates are proportional to the sums of the neglected singularvalues. Numerical experiments illustrate the theoretical results.
We construct an approximate Riemann solver for the isentropic Baer−Nunziato two-phaseflow model, that is able to cope with arbitrarily small values of the statistical phasefractions. The solver relies on a relaxation approximation of the model for which theRiemann problem is exactly solved for subsonic relative speeds. In an original manner, theRiemann solutions to the linearly degenerate relaxation system are allowed to dissipatethe total energy in the vanishing phase regimes, thereby enforcing the robustness andstability of the method in the limits of small phase fractions. The scheme is proved tosatisfy a discrete entropy inequality and to preserve positive values of the statisticalfractions and densities. The numerical simulations show a much higher precision and a morereduced computational cost (for comparable accuracy) than standard numerical schemes usedin the nuclear industry. Finally, two test-cases assess the good behavior of the schemewhen approximating vanishing phase solutions.
Inspired by the growing use of non linear discretization techniques for the lineardiffusion equation in industrial codes, we construct and analyze various explicit nonlinear finite volume schemes for the heat equation in dimension one. These schemes areinspired by the Le Potier’s trick [C. R. Acad. Sci. Paris, Ser. I348 (2010) 691–695]. They preserve the maximum principle and admita finite volume formulation. We provide a original functional setting for the analysis ofconvergence of such methods. In particular we show that the fourth discrete derivative isbounded in quadratic norm. Finally we construct, analyze and test a new explicit nonlinear maximum preserving scheme with third order convergence: it is optimal on numericaltests.
We prove uniformcontinuity ofradiallysymmetric vector minimizersuA(x) = UA(|x|)to multiple integrals ∫BRL**(u(x), |Du(x)|) dx on aballBR ⊂ ℝd,among the Sobolev functions u(·) in A+W01,1 (BR, ℝm), using ajointlyconvexlscL∗∗ : ℝm×ℝ → [0,∞]withL∗∗(S,·) evenand superlinear. Besides such basic hypotheses,L∗∗(·,·) is assumed to satisfy alsoa geometrical constraint, which we callquasi − scalar; the simplest example being thebiradial caseL∗∗(|u(x)|,|Du(x)|).Complete liberty is given forL∗∗(S,λ)to take the ∞ value, so that our minimization problem implicitly also representse.g. distributed-parameteroptimalcontrol problems, onconstraineddomains, under PDEs or inclusions inexplicit or implicit form. While generic radial functionsu(x) = U(|x|) inthis Sobolev space oscillate wildly as |x| → 0, our minimizingprofile-curve UA(·) is, incontrast, absolutelycontinuous andtame, in the sense that its“staticlevel” L∗∗(UA(r),0)always increases with r, a original feature of our result.
The aim of this paper is to answer the question: Do the controls of a vanishing viscosity approximation of the one dimensional linear wave equation converge to a control of the conservative limit equation? The characteristic of our viscous term is that it contains the fractional power α of the Dirichlet Laplace operator. Through the parameter α we may increase or decrease the strength of the high frequencies damping which allows us to cover a large class of dissipative mechanisms. The viscous term, being multiplied by a small parameter ε devoted to tend to zero, vanishes in the limit. Our analysis, based on moment problems and biorthogonal sequences, enables us to evaluate the magnitude of the controls needed for each eigenmode and to show their uniform boundedness with respect to ε, under the assumption that α∈[0,1)\{½}. It follows that, under this assumption, our starting question has a positive answer.
This note considers an established reaction–diffusion model for a combustion system, in which there are competing endothermic and exothermic reaction pathways. A combustion front is assumed to move at constant speed through the medium. An asymptotic theory is presented for solid fuels in which material diffusion is ignored, and it allows a simple and complete analysis of the approximate system in the phase plane. Both the adiabatic and nonadiabatic cases are discussed.
Complexity science is the study of systems with many interdependent components. One of the main concepts is “emergence”: the whole may be greater than the sum of the parts. The objective of this chapter is to put emergence on a firm mathematical foundation in the context of dynamics of large networks. Both stochastic and deterministic dynamics are treated. To minimise technicalities, attention is restricted to dynamics in discrete time, in particular to probabilistic cellular automata and coupled map lattices. The key notion is space-time phases: probability distributions for state as a function of space and time that can arise in systems that have been running for a long time. What emerges from a complex dynamic system is one or more space-time phases. The amount of emergence in a space-time phase is its distance from the set of product distributions over space, using an appropriate metric. A system exhibits strong emergence if it has more than one space-time phase. Strong emergence is the really interesting case.
This chapter is based on MSc or PhD courses given at Warwick in 2006/7, Paris in April 2007, Warwick in Spring 2009 and Autumn 2009, and Brussels in Autumn 2010. It was written up during study leave in 2010/11 at the Université Libre de Bruxelles, to whom I am grateful for hospitality, and finalised in 2012.
The chapter provides an introduction to the theory of space-time phases, via some key examples of complex dynamic system.
I am most grateful to Dayal Strub for transcribing the notes into LaTeX and for preparing the figures.
Microbial competition for nutrients is a common phenomenon that occurs between species inhabiting the same environment. Bioreactors are often used for the study of microbial competition since the number and type of microbial species can be controlled, and the system can be isolated from other interactions that may occur between the competing species. A common type of competition is the so-called “pure and simple” competition, where the microbial populations interact in no other way except the competition for a single rate-limiting nutrient that affects their growth rates. The issue of whether pure and simple competition under time-invariant conditions can give rise to chaotic behaviour has been unresolved for decades. The third author recently showed, for the first time, that chaos can theoretically occur in these systems by analysing the dynamics of a model where both competing species grow following the biomass-dependent Contois model while the yield coefficients associated with the two species are substrate-dependent. In this paper we show that chaotic behaviour can occur in a much simpler model of pure and simple competition. We examine the case where only one species grows following the Contois model with variable yield coefficient while the other species is allowed to grow following the simple Monod model with constant yield. We show that while the static behaviour of the proposed model is quite simple, the dynamic behaviour is complex and involves period doubling culminating in chaos. The proposed model could serve as a basis to re-examine the importance of Contois kinetics in predicting complex behaviour in microbial competition.
Economic behavior and market evolution present notoriously difficult complex systems, where physical interacting particles become purpose-pursuing interacting agents, thus providing a kind of a bridge between physics and social sciences.
We systematically develop the mathematical content of the basic theory of financial economics that can be presented rigorously using elementary probability and calculus, that is, the notions of discrete and absolutely continuous random variables, their expectation, notions of independence and of the law of large numbers, basic integration – differentiation, ordinary differential equations and (only occasionally) the method of Lagrange multipliers. We do not assume any knowledge of finance, apart from an elementary understanding of the idea of compound interest, which can be of two types: (i) simple compounding with rate r and a fixed period of time means your capital in this period is multiplied by (1 + r); (ii) continuous compounding with rate r means your capital in a period of time of length t is multiplied by ert.
This chapter is based on several lecture courses for statistics and mathematics students at the University of Warwick and on invited mini-courses presented by the author at various other places. Sections 6.2 and 6.3 are developed from the author's booklet [9]. The chapter is written in a rather concise (but comprehensive) style in attempt to pin down as clear as possible the mathematical relations that govern the laws of financial economics. Numerous heavy volumes are devoted to the detail discussion of the economic content of these mathematical relations, see e.g. [5], [6], [8], [15], [17].
Partial differential equations in complexity science
Partial differential equations (PDEs), that is to say equations relating partial derivatives of functions of more than one variable, are part of the bedrock of most quantitative disciplines and complexity science is no exception. They invariably arise when continuous fields are introduced into models. The classic example is the distribution of heat in a thermal conductor which typically varies continuously with time and with position. The continuous field in this case is T(x,t) the temperature at position x and time t which, in this case, satisfies a linear PDE called the diffusion equation.
In complexity science, PDEs often result from the process of coarse-graining whereby microscopically discrete processes are averaged over small scales to produce an effective continuous description of larger scales. Since much of complexity science focuses on the emergent properties of such coarse-grained descriptions, the analysis of PDEs is a key part of our complexity science toolkit. Some examples of coarse-graining as applied to interacting particle systems were discussed in Chapter 3. Another well-known example is the effective description of traffic flow using a coarse-grained fluid description described by a PDE known as Burgers' equation and variants of it, cf. Chapter 4. We will revisit this application in more detail later. Real fluids also provide a wealth of examples of complex behaviour, all described by the well-known Navier-Stokes equations or variants of them which are famous for being among the most mathematically intractable PDEs of classical physics.
Dynamical systems are represented by mathematical models that describe different phenomena whose state (or instantaneous description) changes over time. Examples are mechanics in physics, population dynamics in biology and chemical kinetics in chemistry. One basic goal of the mathematical theory of dynamical systems is to determine or characterise the long-term behaviour of the system using methods of bifurcation theory for analysing differential equations and iterated mappings. Interestingly, some simple deterministic nonlinear dynamical systems and even piecewise linear systems can exhibit completely unpredictable behaviour, which might seem to be random. This behaviour of systems is known as deterministic chaos.
This chapter aims to introduce some of the techniques used in the modern theory of dynamical systems and the concepts of chaos and strange attractors, and to illustrate a range of applications to problems in the physical, biological and engineering sciences. The material covered includes differential (continuous-time) and difference (discrete-time) equations, first- and higher order linear and nonlinear systems, bifurcation analysis, nonlinear oscillations, perturbation methods, chaotic dynamics, fractal dimensions, and local and global bifurcation.
Readers are expected to know calculus and linear algebra and be familiar with the general concept of differential equations.
Many examples exist of systems made of a large number of comparatively simple elementary constituents which exhibit interesting and surprising collective emergent behaviours. They are encountered in a variety of disciplines ranging from physics to biology and, of course, economics and social sciences. We all experience, for instance, the variety of complex behaviours emerging in social groups. In a similar sense, in biology, the whole spectrum of activities of higher organisms results from the interactions of their cells and, at a different scale, the behaviour of cells from the interactions of their genes and molecular components. Those, in turn, are formed, as all the incredible variety of natural systems, from the spontaneous assembling, in large numbers, of just a few kinds of elementary particles (e.g., protons, electrons).
To stress the contrast between the comparative simplicity of constituents and the complexity of their spontaneous collective behaviour, these systems are sometimes referred to as “complex systems”. They involve a number of interacting elements, often exposed to the effects of chance, so the hypothesis has emerged that their behaviour might be understood, and predicted, in a statistical sense. Such a perspective has been exploited in statistical physics, as much as the later idea of “universality”. That is the discovery that general mathematical laws might govern the collective behaviour of seemingly different systems, irrespective of the minute details of their components, as we look at them at different scales, like in Chinese boxes.