79 citations · 258 across the 61 of their papers we have counts for
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math.NA2020
Multilevel quasi-Monte Carlo for random elliptic eigenvalue problems I: Regularity and error analysis
Alexander D. Gilbert, Robert Scheichl
Stochastic PDE eigenvalue problems are useful models for quantifying the uncertainty in several applications from the physical sciences and engineering, e.g., structural vibration…
math.NA2020★ 1 cited
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck +1
Low-rank tensor approximations have shown great potential for uncertainty quantification in high dimensions, for example, to build surrogate models that can be used to speed up lar…