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Approximations of stochastic and multi-parametric differential equations may lead to extremely high dimensional problems that suffer from the so called curse of dimensionality.Computational tractability may be recovered by relying on adaptive low-rank/sparse approximation.The tasks are 1)to keep a low-rank approximation of the high-dimensional input data through the whole computing process,2)compute the solution and perform a post-processing in a low-rank tensor format.The post-processing may include computation of different statistics,visualization of a small portion of large data,large data analysis.