Finite Element Analysis

  • Kendall Atkinson
  • Weimin Han
Part of the Texts in Applied Mathematics book series (TAM, volume 39)


The finite element method is the most popular numerical method for solving elliptic boundary value problems. In this chapter, we introduce the concept of the finite element method, the finite element interpolation theory and its application in error estimates of finite element solutions of elliptic boundary value problems. The boundary value problems considered in this chapter are linear.

From the discussion in the previous chapter, we see that the Galerkin method for a linear boundary value problem reduces to the solution of a linear system. In solving the linear system, properties of the coefficient matrix A play an essential role. For example, if the condition number of A is too big, then from a practical perspective, it is impossible to find directly an accurate solution of the system (see [15]). Another important issue is the sparsity of the matrix A. The matrix A is said to be sparse, if most of its entries are zero; otherwise the matrix is said to be dense. Sparseness of the matrix can be utilized for two purposes. First, the stiffness matrix is less costly to form (observing that the computation of each entry of the matrix involves a domain integration and sometimes a boundary integration as well). Second, if the coefficient matrix is sparse, then the linear system can usually be solved more efficiently. To get a sparse stiffness matrix with the Galerkin method, we use finite dimensional approximation spaces such that it is possible to choose basis functions with small support. This consideration gives rise to the idea of the finite element method, where we use piecewise (images of) smooth functions (usually polynomials) for approximations. Loosely speaking, the finite element method is a Galerkin method with the use of piecewise (images of) polynomials.


Finite Element Analysis Element Solution Element Space Interpolation Error Regular Family 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.Department of Mathematics & Computer ScienceUniversity of IowaIowa CityUSA
  2. 2.Department of MathematicsUniversity of Iowa CityIowa CityUSA

Personalised recommendations