This Master thesis describes the design and implementation of particle system and a user interface of tool for fireworks design and simulation. The engine uses an XNA framework and an HLSL shading language. The thesis also compares applications focused on designing and simulation of fireworks. Applicability and demandings of applications for designing and simulation of fireworks  among Czech and Slovak companies  dealing professionally with firework  are  analysed as well.

For a general attractive Probabilistic Cellular Automata on S-Zd, we prove that the (time-) convergence towards equilibrium of this Markovian parallel dynamics, exponentially fast in the uniform norm, is equivalent to a condition (A). This condition means the exponential decay of the influence from the boundary for the invariant measures of the system restricted to finite boxes. For a class of reversible PCA dynamics on {1,+1}(Zd), wit a naturally associated Gibbsian potential rho, we prove that a (spatial-) weak mixing condition (WM) for rho implies the validity of the assumption (A); thus exponential (time-) ergodicity of these dynamics towards the unique Gibbs measure associated to rho hods. On some particular examples we state that exponential ergodicity holds as soon as there is no phase transition.

Les résultats de cette thèse sont composés de trois parties relativement indépendantes.Dans la première partie, nous reprenons le problème de la définition d'une classe de processus markoviens à une infinité de coordonnées (systèmes de particules en interaction). Nous en proposons une construction ne mettant en jeu ni d'analyse fonctionnelle (ou peu), ni de problème de martingale. Ceci est fait en utilisant des outils probabilistes élémentaires, notamment des couplages adéquats. On fait pour cela une certaine hypothèse sur les taux individuels de transition, qui a été déjà exploitée dans la construction de T. M. Liggett (1972) notamment. Notre construction a l'avantage d'expliquer, plus concrètement que dans les autres constructions, le caractère naturel de cette hypothèse.Dans une seconde partie, nous considérons un modèle de croissance cristalline introduit par D. J. Gates et M. Westcott en 1987, où des particules du milieu environnant s'agrègent à la surface d'un cristal à maille carrée. Le modèle est caractérisé par des taux de déposition en chaque site qui prennent une certaine forme. Nos résultats portent principalement sur la question de la récurrence et de la récurrence positive de la surface du cristal en fonction de certains paramètres. Nous montrons notamment l'existence d'une zone de paramètres dans laquelle transience et récurrence positive coexistent, et suspectée de présenter un phénomène critique. / The results of this thesis are organized in three parts that are nearly independent.In the first part, we treat the problem of the defintion of a class of Markov processes with infinitely many coordinates, namely interacting particle systems. We propose a construction involving neither functional analysis, nor martingale problems. This is done using elementary probabilistic tools, such as proper couplings. Our technique requires a certain assumption on the jump rates which is, up to a slight generalization, the one used in T. M. Liggett's construction. Our construction has the advantage to give more intuition on the necessity of this assumption.In the second part, we consider a crystal growth model proposed by D. J. Gates and M. Westcott in 1987, where floating particles are packed on the surface of a square-lattice crystal, with prescribed deposition rates. We treat the question of the recurrence and positive recurrence of the interface, according to the value of certain parameters. We study especially a zone of parameters where transience and positive recurrence coexist. In this zone a critical phenomenon is suspected to occur.The third part deals with the question of the convergence in law for the subcritical contact process (on ZZ) seen from the edge, starting from a half-line of occupied sites. First we give an alternative proof of a recent result by E. D. Andjel, stating that convergence holds in a closely related discrete-time model. In continuous time we establish that the finite contact process seen from the edge has a Yaglom limit.

abstract: This dissertation investigates the dynamics of evolutionary games based on the framework of interacting particle systems in which individuals are discrete, space is explicit, and dynamics are stochastic. Its focus is on 2-strategy games played on a d-dimensional integer lattice with a range of interaction M. An overview of related past work is given along with a summary of the dynamics in the mean-field model, which is described by the replicator equation. Then the dynamics of the interacting particle system is considered, first when individuals are updated according to the best-response update process and then the death-birth update process. Several interesting results are derived, and the differences between the interacting particle system model and the replicator dynamics are emphasized. The terms selfish and altruistic are defined according to a certain ordering of payoff parameters. In these terms, the replicator dynamics are simple: coexistence occurs if both strategies are altruistic; the selfish strategy wins if one strategy is selfish and the other is altruistic; and there is bistability if both strategies are selfish. Under the best-response update process, it is shown that there is no bistability region. Instead, in the presence of at least one selfish strategy, the most selfish strategy wins, while there is still coexistence if both strategies are altruistic. Under the death-birth update process, it is shown that regardless of the range of interactions and the dimension, regions of coexistence and bistability are both reduced. Additionally, coexistence occurs in some parameter region for large enough interaction ranges. Finally, in contrast with the replicator equation and the best-response update process, cooperators can win in the prisoner's dilemma for the death-birth process in one-dimensional nearest-neighbor interactions. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2016

abstract: Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, particles only have local information and have modest computational power. They achieve locomotion by expanding and contracting, which resembles the behavior of amoeba. Under this model, efficient local-control algorithms for the leader election problem in SOPS are presented. As a central problem for programmable matter, shape formation problems are then studied. The limitations of solving the leader election problem and the shape formation problem on a more general version of the amoebot model are also discussed. The \smart paint" problem is also studied which aims at having the particles self-organize in order to uniformly coat the surface of an object of arbitrary shape and size, forming multiple coating layers if necessary. A Universal Coating algorithm is presented and shown to be asymptotically worst-case optimal both in terms of time with high probability and work. In particular, the algorithm always terminates within a linear number of rounds with high probability. A linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information is presented, which implies that the proposed algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of the proposed algorithm may be better than linear in practice. Developed algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal in terms of the total number of particle movements needed to reach the desired shape configuration. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

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GPGPU (General purpose computing on graphics processing unit) is quite common in today's modern computer games when doing heavy simulation calculations like game physics or particle systems. GPU programming is not only used in games but also in scientific research when doing heavy calculations on molecular structures and protein folding etc. The reason why you use the GPU for these kinds of tasks is that you can gain an incredible speedup in performance to your application. Previous research shows that particle systems scale very well to the GPU architecture. When simulating very large particle-system on the GPU it can run up to 79 times faster than the CPU. But for some very small particle systems the CPU proved to be faster. This research aims to compare the difference between the GPU and CPU when it comes to simulating many smaller particle-systems and to see what happen to the performance when the particle-systems become smaller and smaller.

This thesis is divided into two parts: In the first one (Chapters 1 and 2), we deal with problems arising from quantitative homogenization of the random elliptic operator in divergence form $-\\nabla \\cdot a \\nabla$. In Chapter 1 we study existence and stochastic bounds for the Green function $G$ associated to $-\\nabla \\cdot a \\nabla$ in the case of systems. Without assuming any regularity on the coefficient field $a= a(x)$, we prove that for every (measurable) uniformly elliptic tensor field $a$ and for almost every point $y \\in \\mathbb{R}^d$, there exists a unique Green\''s function centred in $y$ associated to the vectorial operator $-\\nabla \\cdot a\\nabla $ in $\\mathbb^d$, $d> 2$. In addition, we prove that if we introduce a shift-invariant ensemble $\\langle\\cdot \\rangle$ over the set of uniformly elliptic tensor fields, then $\\nabla G$ and its mixed derivatives $\\nabla \\nabla G$ satisfy optimal pointwise $L^1$-bounds in probability. Chapter 2 deals with the homogenization of $-\\nabla \\cdot a \\nabla$ to $-\\nabla \\ah \\nabla$ in the sense that we study the large-scale behaviour of $a$-harmonic functions in exterior domains $\\$ by comparing them with functions which are $\\ah$-harmonic. More precisely, we make use of the first and second-order correctors to compare an $a$-harmonic function $u$ to the two-scale expansion of suitable $\\ah$-harmonic function $u_h$. We show that there is a direct correspondence between the rate of the sublinear growth of the correctors and the smallness of the relative homogenization error $u- u_h$. The theory of stochastic homogenization of elliptic operators admits an equivalent probabilistic counterpart, which follows from the link between parabolic equations with elliptic operators in divergence form and random walks. This allows to reformulate the problem of homogenization in terms of invariance principle for random walks. The second part of thesis (Chapters 3 and 4) focusses on this interplay between probabilistic and analytic approaches and aims at exploiting it to study invariance principles in the case of degenerate random conductance models and systems of interacting particles. In Chapter 3 we study a random conductance model where we assume that the conductances are independent, stationary and bounded from above but not uniformly away from $0$. We give a simple necessary and sufficient condition for the relaxation of the environment seen by the particle to be diffusive in the sense of every polynomial moment. As a consequence, we derive polynomial moment estimates on the corrector which imply that the discrete elliptic operator homogenises or, equivalently, that the random conductance model satisfies a quenched invariance principle. In Chapter 4 we turn to a more complicated model, namely the symmetric exclusion process. We show a diffusive upper bound on the transition probability of a tagged particle in this process. The proof relies on optimal spectral gap estimates for the dynamics in finite volume, which are of independent interest. We also show off-diagonal estimates of Carne-Varopoulos type.

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This thesis focus on the effect that selection has on the ancestry of a spatially structured population. In the absence of selection, the ancestry of a sample from the population behaves as a system of random walks that coalesce upon meeting. Backwards in time, each ancestral lineage jumps, at the time of its birth, to the location of its parent, and whenever two ancestral lineages have the same parent they jump to the same location and coalesce. Introducing selective forces to the evolution of a population translates into branching when we follow ancestral lineages, a by-product of biased sampling forwards in time. We study populations that evolve according to the Spatial Lambda-Fleming-Viot process with selection. In order to assess whether the picture under selection differs from the neutral case we must consider the timescale dictated by the neutral mutation rate Theta. Thus we look at the rescaled dual process with n=1/Theta. Our goal is to find a non-trivial rescaling limit for the system of branching and coalescing random walks that describe the ancestral process of a population. We show that the strength of selection (relative to the mutation rate) required to do so depends on the dimension; in one and two dimensions selection needs to be stronger in order to leave a detectable trace in the population. The main results in this thesis can be summarised as follows. In dimensions three and higher we take the selection coefficient to be proportional to 1/n, in dimension two we take it to be proportional to log(n)/n and finally, in dimension one we take the selection coefficient to be proportional to 1/sqrt(n). We then proceed to prove that in two and higher dimensions the ancestral process of a sample of the population converges to branching Brownian motion. In one dimension, provided we do not allow ancestral lineages to jump over each other, the ancestral process converges to a subset of the Brownian net. We also provide numerical results that show that the non-crossing restriction in one dimension cannot be lifted without a qualitative change in the behaviour of the process. Finally, through simulations, we study the rate of convergence in the two-dimensional case.