Parametric Problems, Stochastic, and Identification By Prof. Hermann Matthies (ISCTUB, Germany) Prof. Hermann Matthies, Institute of Scientific Computing TU Braunschweig, Geramany Mar 6, 15:00 - 16:00 B1 R4102 Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily incorporating new information, e. g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure.
Scalable Hierarchical Algorithms for eXtreme Computing Workshop David Keyes, Professor, Applied Mathematics and Computational Science Apr 28, 08:00 - Apr 30, 16:00 KAUST scientific computing The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.
