Spectral convergence of probability densities for forward problems in uncertainty quantification
نویسندگان
چکیده
The estimation of probability density functions (PDF) using approximate maps is a fundamental building block in computational probability. We consider forward problems uncertainty quantification: the inputs or parameters deterministic model are random with known distribution. scalar quantity interest fixed measurable function parameters, and therefore variable as well. Often, map not explicitly difficult to compute. Hence, problem design good approximation (surrogate model) interest. For goal approximating moments interest, there well developed body research. One widely popular approach generalized polynomial chaos (gPC) its many variants, which spectral accuracy. However, it clear whether PDF can be approximated accuracy This result does follow directly from spectrally accurate moment estimation. In this paper, we prove convergence rates for PDFs collocation Galerkin gPC methods Legendre polynomials all dimensions. particular, exponential densities guaranteed analytic quantities one dimension, provide more refined results stronger rates, an alternative proof strategy based on optimal-transport techniques.
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ژورنال
عنوان ژورنال: Numerische Mathematik
سال: 2022
ISSN: ['0945-3245', '0029-599X']
DOI: https://doi.org/10.1007/s00211-022-01281-4