L'étude des systèmes aux équations différentielles ordinaires vise à prédire le futur des systèmes considérés. La connaissance de l'évolution dans le temps de toutes les variables d' état du modèle permet de prédire de possibles changements radicaux des variables ou des défaillances, par exemple, un moteur peut exploser, un pont peut s'écrouler, une voiture peut se mettre à consommer plus d'essence. De plus, les systèmes dynamiques peuvent contenir des dérivées spatiales et leur discrétisation peut ajouter un très grand nombre d'équations. La résolution des équations différentielles ordinaires est alors une étape essentielle dans la construction des systèmes physiques en terme de dimensionnement et de faisabilité. Le solveur de tels systèmes EDOs doit être rapide, précis et pertinent.En pratique, il n'est pas possible de trouver une fonction continue qui soit solution exacte du problème EDO. C'est pourquoi, des méthodes numériques sont utilisées afin de donner des solutions discrèes qui approchent la solution continue avec une erreur contrôlable. La gestion précise de ce contrôle est très importante afin d'obtenir une solution pertinente en un temps raisonnable.Cette thèse développe un nouveau solveur qui utilise plusieurs méthodes d'amélioration de la vitesse d'exécution des systèmes EDOs. La première méthode est l'utilisation d'un nouveau schéma numérique. Le but est de minimiser le coût de l'intégration en produisant une erreur qui soit le plus proche possible de la tolérance maximale permise par l'utilisateur du solveur. Une autre méthode pour améliorer la vitesse d'exécution est de paralléliser le solveur EDO en utilisant une architecture multicoeur et multiprocesseur. Enfin, le solveur a été testé avec différentes applications d'OpenModelica. / The study of systems of Ordinary Differential Equations aims at predicting the future of the considered systems. The access to the evolution of all states of a system's model allows us to predict possible drastic shifts of the states or failures, e.g. an engine blowing up, a bridge collapsin, a car consuming more gasoline etc. Solving ordinary differential equations is then an essential step of building industrial physical systems in regard to dimensioning and reliability. The solver of such ODE systems needs to be fast, accurate and relevant.In practice, it is not possible to find a continuous function as the exact solution of the real ODE problem. Consequently numerical methods are used to give discrete solutions which approximates the continuous one with a controllable error. The correct handline of this control is very important to get a relevant solution within an acceptable recovery time. Starting from existing studies of local and global errors, this thesis work goes more deeply and adjusts the time step of the integration time algorithm and solves the problem in a very efficient manner.A new scheme is proposed is this thesis, to minimize the cost of integration. Another method to improve the execution speed is to parallelize the ODE solver by using a multicore and a multiprocessor architecture. Finally, the solver has been tested with different applications from OpenModelica.

In this dissertation, a new numerical method for solving some problems on the semiinfinite domain is presented. The method is based upon the modified rational Bernoulli functions. These functions are first introduced. Operational matrices of derivative and product of modified rational Bernoulli functions are then derived and are utilized to reduce the solution of the equations to a system of algebraic equations. This method is used to solve the following problems: Lane-Emden type equations, Volterra’s population model, Blasius equation, and MHD Falkner-Skan equation. Illustrative examples are included to demonstrate the validity and applicability of the technique.

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Many chemical engineering applications, such as safety verification and parameter estimation, require global optimization of dynamic models. Global optimization algorithms typically require obtaining global bounding information of the dynamic system, to aid in locating and verifying the global optimum. The typical approach for providing these bounds is to generate convex relaxations of the dynamic system and minimize them using a local optimization solver. Tighter convex relaxations typically lead to tighter lower bounds, so that the number of iterations in global optimization algorithms can be reduced. To carry out this local optimization efficiently, subgradient-based solvers require gradients or subgradients to be furnished. Smooth convex relaxations would aid local optimization even more. To address these issues and improve the computational performance of global dynamic optimization, this thesis proposes several novel formulations for constructing tight convex relaxations of dynamic systems. In some cases, these relaxations are smooth. Firstly, a new strategy is developed to generate convex relaxations of implicit functions, under minimal assumptions. These convex relaxations are described by parametric programs whose constraints are convex relaxations of the residual function. Compared with established methods for relaxing implicit functions, this new approach does not assume uniqueness of the implicit function and does not require the original residual function to be factorable. This new strategy was demonstrated to construct tighter convex relaxations in multiple numerical examples. Moreover, this new convex relaxation strategy extends to inverse functions, feasible-set mappings in constraint satisfaction problems, as well as parametric ordinary differential equations (ODEs). Using a proof-of-concept implementation in Julia, numerical examples are presented to illustrate the convex relaxations produced for various implicit functions and optimal-value functions. In certain cases, these convex relaxations are tighter than those generated with existing methods. Secondly, a novel optimization-based framework is introduced for computing time-varying interval bounds for ODEs. Such interval bounds are useful for constructing convex relaxations of ODEs, and tighter interval bounds typically translate into tighter convex relaxations. This framework includes several established bounding approaches, but also includes many new approaches. Some of these new methods can generate tighter interval bounds than established methods, which are potentially helpful for constructing tighter convex relaxations of ODEs. Several of these approaches have been implemented in Julia. Thirdly, a new approach is developed to improve a state-of-the-art ODE relaxation method and generate tighter and smooth convex relaxations. Unlike state-of-the-art methods, the auxiliary ODEs used in these new methods for computing convex relaxations have continuous right-hand side functions. Such continuity not only makes the new methods easier to implement, but also permits the evaluation of the subgradients of convex relaxations. Under some additional assumptions, differentiable convex relaxations can be constructed. Besides that, it is demonstrated that the new convex relaxations are at least as tight as state-of-the-art methods, which benefits global dynamic optimization. This approach has been implemented in Julia, and numerical examples are presented. Lastly, a new approach is proposed for generating a guaranteed lower bound for the optimal solution value of a nonconvex optimal control problem (OCP). This lower bound is obtained by constructing a relaxed convex OCP that satisfies the sufficient optimality conditions of Pontryagin's Minimum Principle. Such lower bounding information is useful for optimizing the original nonconvex OCP to a global minimum using deterministic global optimization algorithms. Compared with established methods for underestimating nonconvex OCPs, this new approach constructs tighter lower bounds. Moreover, since it does not involve any numerical approximation of the control and state trajectories, it provides lower bounds that are reliable and consistent. This approach has been implemented for control-affine systems, and numerical examples are presented. / Thesis / Doctor of Philosophy (PhD)

The method of dual steepest descent is used to solve ordinary differential equations with nonlinear boundary conditions. A general boundary condition is B(u) = 0 where where B is a continuous functional on the nth order Sobolev space Hn[0.1J. If F:HnCO,l] —• L2[0,1] represents a 2 differential equation, define *(u) = 1/2 IIF < u) li and £(u) = 1/2 l!B(u)ll2. Steepest descent is applied to the functional 2 £ a * + £. Two special cases are considered. If f:lR —• R is C^(2), a Type I boundary condition is defined by B(u) = f(u(0),u(1)). Given K: [0,1}xR—•and g: [0,1] —• R of bounded variation, a Type II boundary condition is B(u) = ƒ1/0K(x,u(x))dg(x).

No / Surface blending with tangential continuity is most widely applied in computer-aided design, manufacturing systems, and geometric modeling. In this paper, we propose a new blending method to effectively control the shape of blending surfaces, which can also satisfy the blending constraints of tangent continuity exactly. This new blending method is based on the concept of swept surfaces controlled by a vector-valued fourth order ordinary differential equation (ODE). It creates blending surfaces by sweeping a generator along two trimlines and making the generator exactly satisfy the tangential constraints at the trimlines. The shape of blending surfaces is controlled by manipulating the generator with the solution to a vector-valued fourth order ODE. This new blending methods have the following advantages: (1) exact satisfaction of C1C1 continuous blending boundary constraints, (2) effective shape control of blending surfaces, (3) high computing efficiency due to explicit mathematical representation of blending surfaces, and (4) ability to blend multiple (more than two) primary surfaces.

The cell cycle of eukaryotes consists of alternation between growth and DNA replication (interphase), and DNA distribution and cell-division (mitosis or M-phase). This process is regulated by a complex network of biochemical reactions. A core part of this network, called the "Cell Cycle engine" is evolutionarily conserved. The dimer of CDK1 (a protein kinase) and Cyclin proteins (the regulatory components), called M-phase Promoting Factor (MPF), and its key regulatory proteins Cdc25 and Wee1, are central parts of this cell cycle engine. Maintaining the fidelity of the DNA during the cell cycle is critical for successful propagation of the cell lineage. In the presence of unreplicated DNA, the cell cycle engine''s progress into mitosis is slowed down (or halted) by regulation of MPF activity through Cdc25 and Wee1. This regulatory event, called the unreplicated DNA checkpoint, was modeled in a rudimentary fashion in the Novak and Tyson (1993) model of frog eggs. Since then, many new experiments have uncovered relevant parts of this network. Here, we include these parts into a detailed model of the unreplicated DNA checkpoint in the cell cycle of frog-egg extracts. This work and future studies of the unreplicated DNA checkpoint will lead to its better understanding and hopefully to some strategies for tackling cancer. / Master of Science

O objetivo deste trabalho e estudar algumas propriedades de soluções de equações diferenciais ordinárias generalizadas e aplicar tais resultados a algumas equações diferenciais clássicas (equações diferenciais ordinárias abstratas e equações diferenciais funcionais em medida). Os principais resultados tratam de existência-unicidade de soluções para uma classe de equações diferenciais ordinárias generalizadas, dependência contnua de soluções com respeito as condições iniciais e bacia de atração. Estes resultados são transferidos para uma classe de equações diferencias ordinárias abstratas. Também obtemos resultados sobre estabilidade da solução trivial de equações diferenciais ordinárias generalizadas e transferimos estes resultados para uma classe de equações diferenciais funcionais em medida / The purpose of this work is to study some properties of solutions of generalized ordinary dierential equations and apply these results to some classical dierential equations (abstract ordinary dierential equations and measure functional dierential equations). The main results concern existence-uniqueness of a solution for a class of generalized ordinary dierential equations, continuous dependence of solutions with respect to initial conditions and basin of attraction. These results are transfered to a class of abstract ordinary dierential equations. We also obtain some results on the stability of the trivial solution of generalized ordinary dierential equations and we transfer these results to a class of measure functional dierential equations

O objetivo deste trabalho e estudar algumas propriedades de soluções de equações diferenciais ordinárias generalizadas e aplicar tais resultados a algumas equações diferenciais clássicas (equações diferenciais ordinárias abstratas e equações diferenciais funcionais em medida). Os principais resultados tratam de existência-unicidade de soluções para uma classe de equações diferenciais ordinárias generalizadas, dependência contnua de soluções com respeito as condições iniciais e bacia de atração. Estes resultados são transferidos para uma classe de equações diferencias ordinárias abstratas. Também obtemos resultados sobre estabilidade da solução trivial de equações diferenciais ordinárias generalizadas e transferimos estes resultados para uma classe de equações diferenciais funcionais em medida / The purpose of this work is to study some properties of solutions of generalized ordinary dierential equations and apply these results to some classical dierential equations (abstract ordinary dierential equations and measure functional dierential equations). The main results concern existence-uniqueness of a solution for a class of generalized ordinary dierential equations, continuous dependence of solutions with respect to initial conditions and basin of attraction. These results are transfered to a class of abstract ordinary dierential equations. We also obtain some results on the stability of the trivial solution of generalized ordinary dierential equations and we transfer these results to a class of measure functional dierential equations

We first discuss some fundamental results such as equilibria, linearization, and stability of nonlinear dynamical systems arising in mathematical modeling. Next we study the dynamics in planar systems such as limit cycles, the Poincaré-Bendixson theorem, and some of its useful consequences. We then study the interaction between two and three different cell populations, and perform stability and bifurcation analysis on the systems. We also analyze the impact of immunotherapy on the tumor cell population numerically.