Kinetic and Related Models doi:10.3934/krm.2017027 c American Institute of Mathematical Sciences Volume 10, Number 3, September 2017 pp. 669{688 FINITE RANGE METHOD OF APPROXIMATION FOR BALANCE LAWS IN MEASURE SPACES Piotr Gwiazda∗, Piotr Orlinski and Agnieszka Ulikowska Institute of Applied Mathematics and Mechanics, University of Warsaw ul. Banacha 2, 02-097 Warsaw, Poland (Communicated by Jos´eAntonio Carrillo) Abstract. In the following paper we reconsider a numerical scheme recently introduced in [10]. The method was designed for a wide class of size structured population models with a nonlocal term describing the birth process. Despite its numerous advantages it features the exponential growth in time of the number of particles constituting the numerical solution. We introduce a new algorithm free from this inconvenience. The improvement is based on the application the Finite Range Approximation to the nonlocal term. We prove the convergence of the derived method and provide the rate of its convergence. Moreover, the results are illustrated by numerical simulations applied to various test cases. 1. Introduction. The aim of this paper is to present an improvement of the nu- merical scheme which was introduced in [10] and which possesses an unfavourable feature: in many cases the number of particles constituting the numerical solution can increase exponentially in time. To deal with the problem we apply a Finite Range Approximation method to the nonlocal term appearing on the right hand side of the model equation (1), see Subsection 2.1 for details. As shown in [10], the convergence of the scheme follows from the stability estimate [9, Theorem 2.11 (ii)]; however, its assumptions are not fulfilled after the application of the approximation procedure. For that reason we need to establish a relaxed version of the stability estimate and apply a new strategy in the proof of convergence of the scheme. The scheme under consideration follows a current trend which is based on a kinetic approach to population dynamics problems [2,3, 17, 21, 22, 23, 26]. Within this approach a population of individuals is divided into groups, so called cohorts. Each cohort is represented by the number of individuals and by their average state. Thus it seems natural to approximate the distribution of the population by a linear combination of Dirac measures. Such a method of approximation is very suitable for numerical studies, especially when it comes to compatibility of a model with experimental data. Indeed, a result of a measurement of a population usually boils down to providing the number of individuals with their average state within the underlying cohort. A good example of such measurements are demographical studies which provide data about a size of the age-cohorts. 2010 Mathematics Subject Classification. P92D25, 65M12, 65M75. Key words and phrases. Structured population models, particle method, measure valued solu- tions, Radon measures, flat metric. ∗ Corresponding author: Piotr Gwiazda. 669 670 PIOTR GWIAZDA, PIOTR ORLINSKI AND AGNIESZKA ULIKOWSKA A broad group of methods originating from the kinetic theory are particle meth- ods, which are designed to model the behavior of large groups of interacting particles or individuals. Over the last decades they have been successfully applied to solve numerically many problems originating from physics as the Euler equation in fluid mechanics [19, 32] and the Vlasov equation in plasma physics [5, 12, 18]. Recently, the particle methods have been used in problems related to crowd dynamics and flow of pedestrians [17, 26, 27], models of a collective motion of large groups of agents [8, 16,4] and population dynamics [9]. For more applications see [24, 25, 28, 29] and references therein. In this paper we focus on the population dynamics and the following size struc- tured population model @ @ Z µ + (b(t; µ)µ) + c(t; µ)µ = (η(t; µ))(y) dµ (y); (1) @t @x R+ where t 2 [0;T ] and x ≥ 0 denote, respectively, time and the size of an individual. Solutions of (1) are considered in a weak sense, see [9, Definition 2.2] for the exact definition. In general, the x variable can describe other physiological states (e.g. length or weight) but for sake of simplicity we stick to the size variable. The measure µ is a distribution of individuals with respect to x. We assume that an individual changes its size according to the following ODE x_ = b(t; µ)(x); where b describes the dynamics of the transformation, that is, the speed of the individual's growth. Function c(t; µ)(x) stands for the death rate, and the integral term describes the birth process. To explain briefly the meaning of the nonlocal term in (1), we assume for a moment that function η does not depend neither on time t nor the population state µ. Then, for a fixed y ≥ 0, function η(y) describes a distribution (with respect to x) of the offspring of individuals at size y. Additionally, in the particular case where all new born individuals have the same size xb we set η(y) = β(y)δx=xb ; (2) where β(y) is related to the probability that individuals at size y procreate. If (2) holds, then the integral in (1) transforms into a boundary condition and, as a consequence, (1) can be reduced to the following classical renewal equation with the nonlocal boundary condition @ @ µ + (bµ) + cµ = 0; for x ≥ x ; @t @x b Z (3) b b b(x )Dλµ(x ) = β(y) dµ (y); R+ b where Dλµ(x ) is the Radon-Nikodym derivative, if it exists, of µ with respect to the Lebesgue measure at xb. The model (1) describes a population which undergoes processes of birth, death and development. The number of individuals in the population and its total biomass change in time, which is clearly indicated by the nonconservative character of the problem. We need to underline that the lack of mass conservation is the main challenge associated with the application of the particle methods in population dy- namics. Let us briefly recall that the most common mathematical framework for the kinetic theory is a space of probability measures equipped with a Wasserstein FINITE RANGE APPROXIMATION METHOD 671 distance. Unfortunately, the 1-Wasserstein distance W1 between two Radon mea- sures µ and ν such that R dµ =6 R dν is infinite, which is the reason why natural distances for measures, like the Wasserstein distances, cannot be exploited in the case of the nonconservative problems. Indeed, let µ, ν be finite Radon measures on R such that µ(R) =6 ν(R). Then, according to [30, Section 7.1] Z W1(µ, ν) = sup '(x) d( µ − ν)(x): Lip(') ≤ 1 R Z ≥ a d( µ − ν)(x) = a(µ(R) − ν(R)); R yields W1(µ, ν) = +1, as a 2 R can be arbitrarily large. Therefore, to consider the well{posedness of the models of population dynamics in the space of measures, the suitable framework is needed in the first place. It has been recently established by replacing the Wasserstein distance by the flat metric (see Section3 for definitions and technical details). One of the first steps in that field has been made in [22, 23], where existence, uniqueness and stability of solutions to (3) in the space of finite, nonnegative Radon measures equipped with the flat metric were proved. Within this framework the first formal proof of convergence of a corresponding particle method for (3) has been conducted in [6]. The method is called the Escalator Boxcar Train (EBT), and although it was described for the first time in the 80's in [13], the proof of its convergence and the convergence rate [20] is very recent. Well{posedness of a general size-structured population model (1) in the space of measures was established in [9], and a numerical scheme based on the particle methods was developed in [10]. In the latter paper an essential assumption is the particular form of the η function, namely r X η(y) = βp(y)δx=fp(y); (4) p=1 r which means that the size of a child belongs to a set ffp(y)gp=1, where y stands for b the size of its parent. For instance, letting r = 1, f1(y) = x , and β1(y) = β > 0 corresponds to the special case of (2), and leads to the equation (3). Another common example is a simple symmetric cell division model, which arises due to the 1 following settings; r = 1, f1(y) = 2 y, and β1(y) = β > 0. The asymmetric case may be obtained with r = 2, f1(y) = σy, f2(y) = (1 − σ)y, where 0 < σ < 1, and β1(y) = β1 > 0, β2(y) = β2 > 0. In both cases the cell division process is understood as the birth of two new cells and the death of a mother cell, which should be incorporated in the death rate. As has been already stated above, in the kinetic approach a solution is approx- imated by a linear combination of Dirac measures at each discrete time moment. In the case of the algorithm developed in [10] Dirac deltas represent cohorts, that is groups of individuals of a similar size. Since a population undergoes a process of births, at least one additional Dirac measure is created at each time step of the algorithm. In case of the particular choice of (2) it is exactly one Dirac measure, since all new born individuals have the same size. However, in the case of the sym- metric cell division model the number of Dirac measures is doubled at each time step, which results in the exponential growth of particles.