Computational Mathematics and Applications

The use of discrete differential forms in the construction of finite element discretisations of the Sobolev spaces H^s, H(div) and H(curl) is now routinely applied by numerical analysts and engineers alike. However, little attention has been paid to the conditioning of the resulting stiffness matrices, particularly in the case of the non-uniform meshes that arise when adaptive refinement algorithms are used. We study this issue and show that the matrices are generally rather poorly conditioned. Typically, diagonal scaling is applied (often unwittingly) as a preconditioner. However, whereas diagonal scaling removes the effect of the mesh non-uniformity in the case of Sobolev spaces H^s, we show this is not so in the case of the spaces H(curl) and H(div). We trace the reason behind this difference, and give a simple remedy for curing the problem.

We consider the solution of sequences of linear systems by preconditioned iterative methods. Such systems arise, for example, in applications such as CFD and structural mechanics. In some cases it is important to avoid the recomputation of preconditioners for subsequent systems. We propose an algebraic strategy that replaces new preconditioners by old preconditioners with simple updates. Efficiency of the new strategy, which generalizes the approach of Benzi and Bertaccini, is demonstrated using numerical experiments.

This talk presents results of joint work with Jurjen Duintjer Tebbens.

In this talk, we report on recent activities in the development of automatic differentiation tools for Matlab and CapeML, a common intermediate language for process control, and highlight some recent AD applications. Lastly, we show the potential for parallelisation created by AD and comment on the impact on scientific computing due to emerging multicore chips which are providing substantial thread-based parallelism in a "pizza box" form factor.