A Galerkin method with two-dimensional Haar basis functions for the computation of the Karhunen–Loève expansion

• Published in: Computational & applied mathematics 2018-05, Vol.37 (2), p.1825-1846
• Main Authors: Azevedo, J. S., Wisniewski, F., Oliveira, S. P.
• Format: Article
• Language: English
• Subjects: Algorithms; Applications of Mathematics; Applied physics; Approximation; Basis functions; Computational mathematics; Computational Mathematics and Numerical Analysis; Discrete Wavelet Transform; Galerkin method; Integral equations; Mathematical Applications in Computer Science; Mathematical Applications in the Physical Sciences; Mathematics; Mathematics and Statistics; Numerical methods; Shape functions; Sparsity; Wavelet transforms
• ISSN: 0101-8205; 2238-3603; 1807-0302
• DOI: 10.1007/s40314-017-0422-4

We study the numerical approximation of a homogeneous Fredholm integral equation of second kind associated with the Karhunen–Loève expansion of Gaussian random fields. We develop, validate, and discuss an algorithm based on the Galerkin method with two-dimensional Haar wavelets as basis functions. The shape functions are constructed from the orthogonal decomposition of tensor-product spaces of one-dimensional Haar functions, and a recursive algorithm is employed to compute the matrix of the discrete eigenvalue system without the explicit calculation of integrals, allowing the implementation of a fast and efficient algorithm that provides considerable reduction in CPU time, when compared with classical Galerkin methods. Numerical experiments confirm the convergence rate of the method and assess the approximation error and the sparsity of the eigenvalue system when the wavelet expansion is truncated. We illustrate the numerical solution of a diffusion problem with random input data with the present method. In this problem, accuracy was retained after dropping the coefficients below a threshold value that was numerically determined. A similar method with scaling functions rather than wavelet functions does not need a discrete wavelet transform and leads to eigenvalue systems with better conditioning but lower sparsity.