Aproximación de ecuaciones diferenciales mediante una nueva técnica variacional y aplicaciones

[SPA] En esta Tesis presentamos el estudio teórico y numérico de sistemas de ecuaciones diferenciales basado en el análisis de un funcional asociado de forma natural al problema original. Probamos que cuando se utiliza métodos del descenso para minimizar dicho funcional, el algoritmo decrece el error hasta obtener la convergencia dada la no existencia de mínimos locales diferentes a la solución original. En cierto sentido el algoritmo puede considerarse un método tipo Newton globalmente convergente al estar basado en una linearización del problema. Se han estudiado la aproximación de ecuaciones diferenciales rígidas, de ecuaciones rígidas con retardo, de ecuaciones algebraico‐diferenciales y de problemas hamiltonianos. Esperamos que esta nueva técnica variacional pueda usarse en otro tipo de problemas diferenciales. [ENG] This thesis is devoted to the study and approximation of systems of differential equations based on an analysis of a certain error functional associated, in a natural way, with the original problem. We prove that in seeking to minimize the error by using standard descent schemes, the procedure can never get stuck in local minima, but will always and steadily decrease the error until getting to the original solution. One main step in the procedure relies on a very particular linearization of the problem, in some sense it is like a globally convergent Newton type method. We concentrate on the approximation of stiff systems of ODEs, DDEs, DAEs and Hamiltonian systems. In all these problems we need to use implicit schemes. We believe that this approach can be used in a systematic way to examine other situations and other types of equations.Universidad Politécnica de Cartagen

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method

BackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities.ResultsIn this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler ?-leap, as well as two more recent ?-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments.ConclusionsThe Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations

Numerical analysis of some integral equations with singularities

In this thesis we consider new approaches to the numerical solution of a class of Volterra integral equations, which contain a kernel with singularity of non-standard type. The kernel is singular in both arguments at the origin, resulting in multiple solutions, one of which is differentiable at the origin. We consider numerical methods to approximate any of the (infinitely many) solutions of the equation. We go on to show that the use of product integration over a short primary interval, combined with the careful use of extrapolation to improve the order, may be linked to any suitable standard method away from the origin. The resulting split-interval algorithm is shown to be reliable and flexible, capable of achieving good accuracy, with convergence to the one particular smooth solution.Supported by a college bursary from the University of Chester

Implicit Runge-Kutta formulae for the numerical integration of ODEs

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SARK: a type-insensitive Runge-Kutta code

A novel solution method based on Mono-implicit Runge-Kutta methods has been fully developed and analysed for the numerical solution of stiff systems of ordinary differential equations (ODE). These Backward Runge-Kutta (BRK) methods have very desirable stability properties which make them efficient for solving a certain class of ODE which are not solved adequately by current methods. These stability properties arise from applying a numerical method to the standard test problem and analysing the resulting stability function. This technique, however, fails to show the full potential of a method. With this in mind a new graphical technique has been derived that examines the methods performance on the standard test case in much greater detail. This technique allows a detailed investigation of the characteristics required for a numerical integration of highly oscillatory problems. Numerical ODE solvers are used extensively in engineering applications, where both stiff and non-stiff systems are encountered, hence a single code capable of integrating the two categories, undetected by the user, would be invaluable. The BRK methods, combined with explicit Runge-Kutta (ERK) methods, are incorporated into such a code. The code automatically determines which integrator can currently solve the problem most efficiently. A switch to the most efficient method is then made. Both methods are closely linked to ensure that overheads expended in the switching are minimal. Switching from ERK to BRK is performed by an existing stiffness detection scheme whereas switching from BRK to ERK requires a new numerical method to be devised. The new methods, called extended BRK (EBRK) methods, are based on the BRK methods but are chosen so as to possess stability properties akin to the ERK methods. To make the code more flexible the switching of order is also incorporated. Numerical results from the type-insensitive code, SARK, indicate that it performs better than the most widely used non-stiff solver and is often more efficient than a specialized stiff solver

Numerical investigations on global error estimation for ordinary differential equations

AbstractFour techniques of global error estimation, which are Richardson extrapolation (RS), Zadunaisky's technique (ZD), Solving for the Correction (SC) and Integration of Principal Error Equation (IPEE) have been compared in different integration codes (DOPRI5, DVODE, DSTEP). Theoretical aspects concerning their implementations and their orders are first given. Second, a comparison of them based on a large number of tests is presented. In terms of cost and precision, SC is a method of choice for one-step methods. It is much more precise and less costly than RS, and leads to the same precision as ZD for half its cost. IPEE can provide the order of the error for a cheap cost in codes based on one-step methods. In multistep codes, only RS and IPEE have been implemented since they are the only ones whose theoretical justification has been extended to this case. There, RS still provides a more reliable estimation than IPEE. However, as these techniques are based on variations of the global error, irrespective of the numerical method used, they fail to provide any more usefull information once the numerical method has reached its limit of accuracy due to the finite arithmetic