Griebel, MichaelMichaelGriebelHarbrecht, HelmutHelmutHarbrechtSchneider, ReinholdReinholdSchneider2023-02-072023-02-072022-03-08https://publica.fraunhofer.de/handle/publica/43567010.48550/arXiv.2203.04100Let Ωi⊂Rni, i=1,…,m, be given domains. In this article, we study the low-rank approximation with respect to L2(Ω1×⋯×Ωm) of functions from Sobolev spaces with dominating mixed smoothness. To this end, we first estimate the rank of a bivariate approximation, i.e., the rank of the continuous singular value decomposition. In comparison to the case of functions from Sobolev spaces with isotropic smoothness, compare \cite{GH14,GH19}, we obtain improved results due to the additional mixed smoothness. This convergence result is then used to study the tensor train decomposition as a method to construct multivariate low-rank approximations of functions from Sobolev spaces with dominating mixed smoothness. We show that this approach is able to beat the curse of dimension.enLow-rank approximationSobolev spaces with dominating mixed smoothnessapproximation errorrank complexityDDC::500 Naturwissenschaften und Mathematikpaper