Reduced-order modeling of parameterized PDEs using time-space-parameter principal component analysis

This paper presents a methodology for constructing low-order surrogate models of finite element/finite volume discrete solutions of parameterized steady-state partial differential equations. The construction of proper orthogonal decomposition modes in both physical space and parameter space allows us to represent high-dimensional discrete solutions using only a few coefficients. An incremental greedy approach is developed for efficiently tackling problems with high-dimensional parameter spaces. For numerical experiments and validation, several non-linear steady-state convection-diffusion-reaction problems are considered: first in one spatial dimension with two parameters, and then in two spatial dimensions with two and five parameters. In the two-dimensional spatial case with two parameters, it is shown that a 7 x 7 coefficient matrix is sufficient to accurately reproduce the expected solution, while in the five parameters problem, a 13 x 6 coefficient matrix is shown to reproduce the Solution with sufficient accuracy. The proposed methodology is expected to find applications to parameter variation Studies, uncertainty analysis, inverse problems and optimal design. Copyright (c) 2009 John Wiley & Sons, Ltd.