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Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence of interest in the modern as well as the clas­ sical techniques of applied mathematics. This renewal of interest, both in research and teaching, has led to the establishment of the series: Texts in Applied Mathematics (TAM). The development of new courses is a natural consequence of a high level of excitement on the research frontier as newer techniques, such as numerical and symbolic computer systems, dynamical systems, and chaos, mix with and reinforce the traditional methods of applied mathematics. Thus, the purpose of this textbook series is to meet the current and future needs of these advances and encourage the teaching of new courses. TAM will publish textbooks suitable for use in advanced undergraduate and beginning graduate courses, and will complement the Applied Math­ (AMS) series, which will focus on advanced textbooks ematical Sciences and research level monographs. Preface This book develops the basic mathematical theory of the finite element method, the most widely used technique for engineering design and analysis. One purpose of this book is to formalize basic tools that are commonly used by researchers in the field but never published. It is intended primarily for mathematics graduate students and mathematically sophisticated engineers and scientists. The book has been the basis for graduate-level courses at The Uni­ versity of Michigan, Penn State University and the University of Houston.

Inhaltsverzeichnis

Frontmatter

Chapter 0. Basic Concepts

Abstract
The finite element method provides a formalism for generating discrete (finite) algorithms for approximating the solutions of differential equations. It should be thought of as a black box into which one puts the differential equation (boundary value problem) and out of which pops an algorithm for approximating the corresponding solutions. Such a task could conceivably be done automatically by a computer, but it necessitates an amount of mathematical skill that today still requires human involvement. The purpose of this book is to help people become adept at working the magic of this black box. The book does not focus on how to turn the resulting algorithms into computer codes, although this is at present also a complicated task. The latter is, however, a more well-defined task than the former and thus potentially more amenable to automation.
Susanne C. Brenner, L. Ridgway Scott

Chapter 1. Sobolev Spaces

Abstract
This chapter is devoted to developing function spaces that are used in the variational formulation of differential equations. We begin with a review of Lebesgue integration theory, upon which our notion of “variational” or “weak” derivative rests. Functions with such “generalized” derivatives make up the spaces commonly referred to as Sobolev spaces. We develop only a small fraction of the known theory for these spaces—just enough to establish a foundation for the finite element method.
Susanne C. Brenner, L. Ridgway Scott

Chapter 2. Variational Formulation of Elliptic Boundary Value Problems

Abstract
This chapter is devoted to the functional analysis tools required for developing the variational formulation of differential equations. It begins with an introduction to Hilbert spaces, including only material that is essential to later developments. The goal of the chapter is to provide a framework in which existence and uniqueness of solutions to variational problems may be established.
Susanne C. Brenner, L. Ridgway Scott

Chapter 3. The Construction of a Finite Element Space

Abstract
To approximate the solution of the variational problem
$$a(u,v) = F(v)\forall v \in V$$
developed in Chapter 0, we need to construct finite-dimensional subspaces SV in a systematic, practical way.
Susanne C. Brenner, L. Ridgway Scott

Chapter 4. Polynomial Approximation Theory in Sobolev Spaces

Abstract
We will now develop the approximation theory appropriate for the finite elements developed in Chapter 3. We take a constructive approach, defining an averaged version of the Taylor polynomial familiar from calculus. The key estimates are provided by some simple lemmas from the theory of Riesz potentials, which we derive. As a corollary, we provide a proof of Sobolev’s inequality, much in the spirit given originally by Sobolev.
Susanne C. Brenner, L. Ridgway Scott

Chapter 5. n-Dimensional Variational Problems

Abstract
We now give several examples of higher-dimensional variational problems that use the theory developed in previous chapters. The basic notation is provided by the Sobolev spaces developed in Chapter 1. We combine the existence theory of Chapter 2 together with the approximation theory of Chapters 3 and 4 to provide a complete theory for the discretization process. Several examples will be fully developed in the text, and several others are found in the exercises. Throughout this chapter, we assume that the domain Ω is bounded.
Susanne C. Brenner, L. Ridgway Scott

Chapter 6. Finite Element Multigrid Methods

Abstract
The multigrid method provides an optimal order algorithm for solving elliptic boundary value problems. The error bounds of the approximate solution obtained from the full multigrid algorithm are comparable to the theoretical bounds of the error in the finite element method, while the amount of computational work involved is proportional only to the number of unknowns in the discretized equations.
Susanne C. Brenner, L. Ridgway Scott

Chapter 7. Max-norm Estimates

Abstract
The finite element approximation is essentially defined by a mean-square projection of the gradient. Thus, it is natural that error estimates for the gradient of the error directly follow in the L 2 norm. It is interesting to ask whether such a gradient-projection would also be of optimal order in some other norm, for example L. We prove here that this is the case. Although of interest in their own right, such estimates are also crucial in establishing the viability of approximations of nonlinear problems (Douglas & Dupont 1975) as we indicate in Sect. 7.7. Throughout this chapter, we assume that the domain,Ω ⊂ ℝ d is bounded and polyhedral.
Susanne C. Brenner, L. Ridgway Scott

Chapter 8. Variational Crimes

Abstract
Consider the Dirichlet problem
$$\begin{array}{*{20}{c}} { - \Delta u = f\,in\,\underline \Omega {{\mathbb{R}}^{2}}} \\ {u = 0\;on\;\partial \Omega } \\ \end{array}$$
(8.0.1)
.
Susanne C. Brenner, L. Ridgway Scott

Chapter 9. Applications to Planar Elasticity

Abstract
In most physical applications, quantities of interest are governed by a system of partial differential equations, not just a single equation. So far, we have only considered single equations (for a scalar quantity), although much of the theory relates directly to systems. We consider one such system coming from solid mechanics in this chapter.
Susanne C. Brenner, L. Ridgway Scott

Chapter 10. Mixed Methods

Abstract
The name “mixed method” is applied to a variety of finite element methods which have more than one approximation space. Typically one or more of the spaces play the role of Lagrange multipliers which enforce constraints. The name and many of the original concepts for such methods originated in solid mechanics where it was desirable to have a more accurate approximation of certain derivatives of the displacement. However, for the Stokes equations which govern viscous fluid flow, the natural Galerkin approximation is a mixed method.
Susanne C. Brenner, L. Ridgway Scott

Chapter 11. Iterative Techniques for Mixed Methods

Abstract
Equations of the form (10.3.1) or (10.5.14) are indefinite and require special care to solve. We will now consider one class of algorithms which involve a penalty method to enforce the second equation in (10.3.1) or (10.5.14). These algorithms transform the linear algebra to positive-definite problems in many cases. Moreover, the number of unknowns in the algebraic system can also be significantly reduced.
Susanne C. Brenner, L. Ridgway Scott

Chapter 12. Applications of Operator-Interpolation Theory

Abstract
Interpolation spaces are useful technical tools. They allow one to bridge between known results, yielding new results that could not be obtained directly. They also provide a concept of fractional-order derivatives, extending the definition of the Sobolev spaces used so far. Such extensions allow one to measure more precisely, for example, the regularity of solutions to elliptic boundary value problems.
Susanne C. Brenner, L. Ridgway Scott

Backmatter

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