This work presents a new hybrid symbolic-numeric method for fast and accurate evaluation of multiple integrals, effective both in high dimensions and with high accuracy. In two dimensions, the thesis presents an adaptive two-phase algorithm for double integration of continuous functions over general regions using Frederick W. Chapman's recently developed Geddes series expansions to approximate the integrand. These results are extended to higher dimensions using a novel Deconstruction/Approximation/Reconstruction Technique (DART), which facilitates the dimensional reduction of families of integrands with special structure over hyperrectangular regions. The thesis describes a Maple implementation of these new methods and presents empirical results and conclusions from extensive testing. Various alternatives for implementation are discussed, and the new methods are compared with existing numerical and symbolic methods for multiple integration. The thesis concludes that for some frequently encountered families of integrands, DART breaks the curse of dimensionality that afflicts numerical integration.

This work presents a new hybrid symbolic-numeric method for fast and accurate evaluation of multiple integrals, effective both in high dimensions and with high accuracy. In two dimensions, the thesis presents an adaptive two-phase algorithm for double integration of continuous functions over general regions using Frederick W. Chapman's recently developed Geddes series expansions to approximate the integrand. These results are extended to higher dimensions using a novel Deconstruction/Approximation/Reconstruction Technique (DART), which facilitates the dimensional reduction of families of integrands with special structure over hyperrectangular regions. The thesis describes a Maple implementation of these new methods and presents empirical results and conclusions from extensive testing. Various alternatives for implementation are discussed, and the new methods are compared with existing numerical and symbolic methods for multiple integration. The thesis concludes that for some frequently encountered families of integrands, DART breaks the curse of dimensionality that afflicts numerical integration.

Doctor of Philosophy / Department of Electrical and Computer Engineering / James E. DeVault / Prakash Krishnaswami / Most state-of-the-art systems are multidisciplinary in nature and encompass a wide range of components from domains such as electronics, mechanics, hydraulics, etc. Design considerations and design parameters of the system can come from any or a combination of these domains. The traditional optimization approach for multidisciplinary systems utilizes sequential optimization, wherein each subsystem is optimized in isolation in a predetermined order, assuming that the designs of the other subsystems remain fixed. This often leads to system designs that are suboptimal. In recent years emphasis has been placed on development of an integrated scheme for analysis and design of multidisciplinary systems. An important aspect is the software architecture required to support such a scheme. This dissertation presents MIXEDMODELS (Multidisciplinary Integrated eXtensible Engine for Driving Metamodeling, Optimization and DEsign of Large-scale Systems) - a unified analysis and design tool for multidisciplinary systems that is based on a procedural, symbolic-numeric architecture. This architecture offers great modeling flexibility at the component level, allowing any engineer to add components in his/her domain of expertise to the platform in a modular fashion. The symbolic engine in the MIXEDMODELS platform synthesizes the system governing equations as a unified set of nonlinear differential-algebraic equations (DAEs). These equations are differentiated with respect to design variables to obtain an additional set of DAEs that describe the sensitivity coefficients of the system state variables. This combined set of DAEs is solved numerically to obtain the solution for the state variables and the state sensitivity coefficients of the system. Finally, knowing the system performance functions, their design sensitivity coefficients can be calculated by using the values of the state variables and state sensitivity coefficients obtained from the DAEs. For ease in error control and software implementation, sensitivity analysis formulation described in this work uses direct differentiation approach as opposed to the adjoint variable approach. The MIXEDMODELS capabilities are demonstrated through several numerical examples and the results indicate that the MIXEDMODELS formulation and architecture is effective in terms of accuracy, modeling convenience, computational efficiency, and the ability to simulate the behavior of a general class of multidisciplinary systems.

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Le calcul rigoureux vise à produire des représentations certifiées pour les solutions de nombreux problèmes, notamment en analyse fonctionnelle, comme des équations différentielles ou des problèmes de contrôle optimal. En effet, certains domaines particuliers comme l’ingénierie des systèmes critiques ou les preuves mathématiques assistées par ordinateur ont des exigences de fiabilité supérieures à ce qui peut résulter de l’utilisation d’algorithmes relevant de l’analyse numérique classique.Notre objectif consiste à développer des algorithmes à la fois efficaces et validés / certifiés, dans le sens où toutes les erreurs numériques (d’arrondi ou de méthode) sont prises en compte. En particulier, nous recourons aux approximations polynomiales rigoureuses combinées avec des méthodes de validation a posteriori à base de points fixes. Ces techniques sont implémentées au sein d’une bibliothèque écrite en C, ainsi que dans un développement de preuve formelle en Coq, offrant ainsi le plus haut niveau de confiance, c’est-à-dire une implémentation certifiée.Après avoir présenté les opérations élémentaires sur les approximations polynomiales rigoureuses, nous détaillons un nouvel algorithme de validation pour des approximations sous forme de séries de Tchebychev tronquées de fonctions D-finies, qui sont les solutions d’équations différentielles ordinaires linéaires à coefficients polynomiaux. Nous fournissons une analyse fine de sa complexité, ainsi qu’une extension aux équations différentielles ordinaires linéaires générales et aux systèmes couplés de telles équations. Ces méthodes dites symboliques-numériques sont ensuite utilisées dans plusieurs problèmes reliés : une nouvelle borne sur le nombre de Hilbert pour les systèmes quartiques, la validation de trajectoires de satellites lors du problème du rendez-vous linéarisé, le calcul de polynômes d’approximation optimisés pour l’erreur d’évaluation, et enfin la reconstruction du support et de la densité pour certaines mesures, grâce à des techniques algébriques. / Rigorous numerics aims at providing certified representations for solutions of various problems, notably in functional analysis, e.g., differential equations or optimal control. Indeed, specific domains like safety-critical engineering or computer-assisted proofs in mathematics have stronger reliability requirements than what can be achieved by resorting to standard numerical analysis algorithms. Our goal consists in developing efficient algorithms, which are also validated / certified in the sense that all numerical errors (method or rounding) are taken into account. Specifically, a central contribution is to combine polynomial approximations with a posteriori fixed-point validation techniques. A C code library for rigorous polynomial approximations (RPAs) is provided, together with a Coq formal proof development, offering the highest confidence at the implementation level.After providing basic operations on RPAs, we focus on a new validation algorithm for Chebyshev basis solutions of D-finite functions, i.e., solutions of linear ordinary differential equations (LODEs) with polynomial coefficients. We give an in-depth complexity analysis, as well as an extension to general LODEs, and even coupled systems of them. These symbolic-numeric methods are finally used in several related problems: a new lower bound on the Hilbert number for quartic systems; a validation of trajectories arising in the linearized spacecraft rendezvous problem; the design of evaluation error efficient polynomial approximations; and the support and density reconstruction of particular measures using algebraic techniques.

Cette thèse porte sur la commande H∞ par loop-shaping pour les systèmes linéaires à temps invariant d'ordre faible avec ou sans retard et dépendant de paramètres inconnus. L'objectif est d'obtenir des correcteurs H∞ paramétriques, c'est-à-dire dépendant explicitement des paramètres inconnus, pour application à des viseurs gyrostabilisés.L'existence de ces paramètres inconnus ne permet plus l'utilisation des techniques numériques classiques pour la résolution du problème H∞ par loop-shaping. Nous avons alors développé une nouvelle méthodologie permettant de traiter les systèmes linéaires de dimension finie grâce à l'utilissation de techniques modernes de calcul formel dédiées à la résolution des systèmes polynomiaux (bases de Gröbner, variétés discriminantes, etc.).Une telle approche présente de multiples avantages: étude de sensibilités du critère H∞ par rapport aux paramètres, identification de valeurs de paramètres singulières ou remarquables, conception de correcteurs explicites optimaux/robustes, certification numérique des calculs, etc. De plus, nous montrons que cette approche peut s'étendre à une classe de systèmes à retard.Plus généralement, cette thèse s'appuie sur une étude symbolique des équations de Riccati algébriques. Les méthodologies génériques développées ici peuvent s'étendre à de nombreux problèmes de l'automatique, notamment la commande LQG, le filtrage de Kalman ou invariant. / This PhD thesis deals with the H∞ loop-shaping design for low order linear time invariant systems depending on unknown parameters. The objective of the PhD thesis is to obtain parametric H∞ controllers, i.e. controllers which depend explicitly on the unknown model parameters, and to apply them to the stabilization of gyrostabilized sights.Due to the unknown parameters, no numerical algorithm can solve the robust control problem. Using modern symbolic techniques dedicated to the solving of polynomial systems (Gröbner bases, discriminant varieties, etc.), we develop a new methodology to solve this problem for finite-dimensional linear systems.This approach shows several advantages : we can study the sensibilities of the H∞ criterion to the parameter variations, identify singular or remarquable values of the parameters, compute controllers which depend explicitly on the parameters, certify the numerical computations, etc. Furthermore, we show that this approach can be extended to a class of linear time-delay systems.More generally, this PhD thesis develops an algebraic approach for the study of algebraic Riccati equations. Thus, the methodology obtained can be extended to many different problems such as LQG control and Kalman or invariant filtering.