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Numerical simulation has become an inevitable tool in most industrial product development processes with simulations being used to understand the influence of design decisions (parameter configurations) on the structure and properties of the product. However, in order to allow the engineer to thoroughly explore the design space and fine-tune parameters, many -- usually very time-consuming -- simulation...
We consider a classification method based on sparse grids which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. In order to obtain competitive results, such sparse grid classifiers are usually enhanced by locally refining the underlying regular sparse grid. However, in order to parallelize the corresponding adaptive algorithms a thorough knowledge...
We present the parallelization of a sparse grid finite element discretization of the Black-Scholes equation, which is commonly used for option pricing. Sparse grids allow to handle higher dimensional options than classical approaches on full grids, and can be extended to a fully adaptive discretization method. We introduce the algorithmical structure of efficient algorithms operating on sparse grids,...
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