Low discrepancy sequences in high dimensions: How well are their projections distributed?

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Least squares polynomial chaos expansion: A review of sampling strategies (2017, methodology): “To alleviate the slow convergence rate of standard MC technique, especially for high-dimensional integrations in ﬁnance applications [120], researchers have proposed a deterministic version of the MC sampling method based on the low-discrepancy sequences [26] known as quasi-Monte Carlo (QMC)…”
Constructing Sobol Sequences with Better Two-Dimensional Projections (2008, background): “See, for example, [25, 26, 27] and the papers cited therein for some recent literature on this topic.”
Measuring Sample Quality with Stein's Method (2015, background): “49 and n − 1 for the i.i.d. and Sobol sequences accord with expected O (1 / √ n ) and O (1 /n ) bounds from the literature [28, 29].”
A unified deep artificial neural network approach to partial differential equations in complex geometries (2017, background): “2 in [32], or [4, 35] and references therein.”