High dimensional approximation

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Using Sparse grids to produce estimates of high dimensional data sets provides an important application of sparse grid techniques. Possible topics include using sparse grid approximations to estimate probability density function, approximations of high dimensional functions, and uncertainty quantification of numerical models (how are model results dependent on parameter change). There are opportunities for mathematical analysis as well as practical implementation issues.

Updated:  24 May 2017/Responsible Officer:  Director/Page Contact:  School Manager