209 research outputs found

    A piecewise-linearized algorithm based on the Krylov subspace for solving stiff ODEs

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    Numerical methods for solving Ordinary Differential Equations (ODEs) have received considerable attention in recent years. In this paper a piecewise-linearized algorithm based on Krylov subspaces for solving Initial Value Problems (IVPs) is proposed. MATLAB versions for autonomous and non-autonomous ODEs of this algorithm have been implemented. These implementations have been compared with other piecewise-linearized algorithms based on Pad approximants, recently developed by the authors of this paper, comparing both precisions and computational costs in equal conditions. Four case studies have been used in the tests that come from stiff biology and chemical kinetics problems. Experimental results show the advantages of the proposed algorithms, especially when the dimension is increased in stiff problems. © 2009 Elsevier B.V. All rights reserved.This work was supported by the Spanish CICYT project CGL2007-66440-C04-03.Ibáñez González, JJ.; Hernández García, V.; Ruiz Martínez, PA.; Arias, E. (2011). A piecewise-linearized algorithm based on the Krylov subspace for solving stiff ODEs. Journal of Computational and Applied Mathematics. 235(7):1798-1804. https://doi.org/10.1016/j.cam.2010.07.012S17981804235

    Nuevos métodos y algoritmos de altas prestaciones para el cálculo de funciones de matrices

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    Tesis por compendio[ES] El objetivo de esta tesis es el desarrollo de algoritmos e implementaciones innovadoras de altas prestaciones (HPC) para la computación de funciones de matrices basadas en series de polinomios matriciales. En concreto, se desarrollarán algoritmos para el cálculo de las funciones matriciales más utilizadas: la exponencial, el seno y el coseno. El estudio de los polinomios ortogonales matriciales es un campo emergente cuyo avance está alcanzando importantes resultados tanto desde el punto de vista teórico como práctico. Las ¿últimas investigaciones realizadas por el doctorando, junto a los miembros del grupo de investigación al que está vinculado, High Performance Scientific Computing (HiPerSC), revelan por qué los polinomios matriciales desempeñan un papel fundamental en la aproximación de funciones de matrices, proporcionando propiedades muy interesantes. En esta tesis se han desarrollado nuevos algoritmos de alto rendimiento basados en series polinomiales matriciales. En particular, se han implementado algoritmos para el cálculo de la exponencial, el seno y el coseno de una matriz usando las series matriciales polinomiales de Taylor y de Hermite. Además, se han proporcionado cotas del error cometido en las aproximaciones calculadas, proporcionando además los parámetros teóricos y experimentales óptimos de dichas aproximaciones. Los algoritmos finales han sido comparados con otras implementaciones del estado del arte para probar la mejora que presentan en cuanto a eficiencia y prestaciones. Los resultados obtenidos a lo largo de la investigación y presentados en esta memoria han sido publicados en varias revistas de alto nivel y se han presentado como ponencias en diversas ediciones del congreso internacional Mathematical Modelling in Engineering & Human Behaviour para dotarlas de la mayor difusión posible. Por otra parte, los códigos informáticos implementados han sido puestos a disposición de la comunidad científica internacional a través de nuestra página web http://hipersc.blogs.upv.es.[CA] L'objectiu d'aquesta Tesi és el desenvolupament d'algoritmes i implementacions innovadores d'altes prestacions (HPC) per a la computació de funcions de matrius basades en sèries de polinomis matricials. En concret, es desenvoluparan algoritmes per al càlcul de les funcions matricials més emprades: l'exponencial, el sinus i el cosinus. L'estudi dels polinomis ortogonals matricials és un camp emergent, el creixement del qual està aconseguint importants resultats tant des del punt de vista teòric com pràctic. Les últimes investigacions realitzades pel doctorand junt amb els membres del grup d'investigació on està vinculat, High Performance Scientific Computing (HiPerSC), revelen per què els polinomis matricials exerceixen un paper fonamental en l'aproximació de funcions de matrius, proporcionant propietats molt interessants. En aquesta Tesi s'han desenvolupat nous algoritmes d'alt rendiment basats en sèries polinomials matricials. En particular, s'han implementat algoritmes per al càlcul de l'exponencial, el sinus i el cosinus d'una matriu usant les sèries matricials polinomials de Taylor i d'Hermite. A més, s'han proporcionat cotes de l'error comès en les aproximacions calculades, proporcionant a més els paràmetres teòrics i experimentals òptims d'aquestes aproximacions. Els algoritmes finals han estat comparats amb altres implementacions de l'estat de l'art per a provar la millora que presenten en termes d'eficiència i prestacions. Els resultats obtinguts al llarg de la investigació i presentats en aquesta memòria han estat publicats en diverses revistes d'alt nivell i s'han presentat com a ponències en diferents edicions del congrés internacional Mathematical Modelling in Engineering \& Human Behaviour per a dotar-les de la major difusió possible. D'altra banda, s'han posat els codis informàtics implementats a disposició de la Comunitat Científica Internacional mitjançant la nostra pàgina web http://hipersc.blogs.upv.es.[EN] The aim of this thesis is the development of high performance computing (HPC) innovative algorithms and implementations for computing matrix functions based on matrix polynomials series. Specifically, algorithms for the calculation of the most commonly-used functions, the exponential, sine and cosine have been developed. The study of orthogonal matrix polynomials is an emerging field whose growth is achieving important results both theoretically and practically. The last investigations made by the doctoral student, together with the members of the research group, High Performance Scientific Computing (HiPerSC), he is linked, reveal why the matrix polynomials play a fundamental role in the approximation of matrix functions, providing very interesting properties.In this thesis new high-performance algorithms based on matrix polynomial series have been developed. In particular, algorithms for computing the exponential, sine and cosine of a matrix using Taylor and Hermite matrix polynomial series have been implemented.In addition, the error bounds for the approximations calculated have been provided and optimal theoretical and experimental parameters for such approximations have also been provided. Final algorithms have been compared to other state of the art implementations to test the improvement obtained in terms of efficiency and performance. The results obtained during the investigation and presented in this memory have been published in several high-level journals and presented as papers at various editions of the International Congress Mathematical Modelling in Engineering & Human Behaviour to give them the widest possible distribution. On the other hand, implemented computer codes have been made freely available to the international scientific community at our web page http://hipersc.blogs.upv.es.Ruiz Martínez, PA. (2020). Nuevos métodos y algoritmos de altas prestaciones para el cálculo de funciones de matrices [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/137035TESISCompendi

    Enhancement of Krylov Subspace Spectral Methods Through the Use of the Residual

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    Depending on the type of equation, finding the solution of a time-dependent partial differential equation can be quite challenging. Although modern time-stepping methods for solving these equations have become more accurate for a small number of grid points, in a lot of cases the scalability of those methods leaves much to be desired. That is, unless the timestep is chosen to be sufficiently small, the computed solutions might exhibit unreasonable behavior with large input sizes. Therefore, to improve accuracy as the number of grid points increases, the time-steps must be chosen to be even smaller to reach a reasonable solution. Krylov subspace spectral (KSS) methods are componentwise, scalable, methods used to solve time-dependent, variable coefficient partial differential equations. The main idea behind KSS methods is to use an interpolating polynomial with frequency dependent interpolation points to approximate a solution operator for each Fourier coefficient. This dissertation will discuss two techniques that were developed to eliminate error in the low frequency components of the solution computed using KSS methods. These two methods are a multigrid inspired technique (coarse grid residual correction) and developing a step size controller using the residual as an error approximation (adaptive time stepping)

    High Resolution Numerical Methods for Coupled Non-linear Multi-physics Simulations with Applications in Reactor Analysis

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    The modeling of nuclear reactors involves the solution of a multi-physics problem with widely varying time and length scales. This translates mathematically to solving a system of coupled, non-linear, and stiff partial differential equations (PDEs). Multi-physics applications possess the added complexity that most of the solution fields participate in various physics components, potentially yielding spatial and/or temporal coupling errors. This dissertation deals with the verification aspects associated with such a multi-physics code, i.e., the substantiation that the mathematical description of the multi-physics equations are solved correctly (both in time and space). Conventional paradigms used in reactor analysis problems employed to couple various physics components are often non-iterative and can be inconsistent in their treatment of the non-linear terms. This leads to the usage of smaller time steps to maintain stability and accuracy requirements, thereby increasing the overall computational time for simulation. The inconsistencies of these weakly coupled solution methods can be overcome using tighter coupling strategies and yield a better approximation to the coupled non-linear operator, by resolving the dominant spatial and temporal scales involved in the multi-physics simulation. A multi-physics framework, KARMA (K(c)ode for Analysis of Reactor and other Multi-physics Applications), is presented. KARMA uses tight coupling strategies for various physical models based on a Matrix-free Nonlinear-Krylov (MFNK) framework in order to attain high-order spatio-temporal accuracy for all solution fields in amenable wall clock times, for various test problems. The framework also utilizes traditional loosely coupled methods as lower-order solvers, which serve as efficient preconditioners for the tightly coupled solution. Since the software platform employs both lower and higher-order coupling strategies, it can easily be used to test and evaluate different coupling strategies and numerical methods and to compare their efficiency for problems of interest. Multi-physics code verification efforts pertaining to reactor applications are described and associated numerical results obtained using the developed multi-physics framework are provided. The versatility of numerical methods used here for coupled problems and feasibility of general non-linear solvers with appropriate physics-based preconditioners in the KARMA framework offer significantly efficient techniques to solve multi-physics problems in reactor analysis

    Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems

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    The present article presents a summarizing view at differential-algebraic equations (DAEs) and analyzes how new application fields and corresponding mathematical models lead to innovations both in theory and in numerical analysis for this problem class. Recent numerical methods for nonsmooth dynamical systems subject to unilateral contact and friction illustrate the topicality of this development.Comment: Preprint of Book Chapte
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