Sparse and low-rank approximations of parametric and stochastic partial differential equations

We discuss recent results on the approximation of solutions of PDEs with parameter-dependent or stochastic coefficients, as well as results and open questions concerning corresponding numerical methods and their computational complexity. In particular, the presented findings reveal mechanisms behind the efficiency of certain model reduction methods and show the influence of the choice of representations of random fields on the approximability of resulting solutions of PDEs.