Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations

DOI10.1016/j.jcp.2018.03.009zbMath1392.65014arXiv1711.09808OpenAlexW2768665740MaRDI QIDQ1656755

Y. Aharonov

Publication date: 10 August 2018

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.09808

zbMATH Keywords

interpolationGrassmann manifolduncertainty quantificationhigh-dimensionalmanifolds of different dimensionmultielement

Mathematics Subject Classification ID

Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (5)

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data ⋮ Data-driven uncertainty quantification in computational human head models ⋮ Grassmannian diffusion maps based surrogate modeling via geometric harmonics ⋮ Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold ⋮ Diffusion maps-aided neural networks for the solution of parametrized PDEs

Uses Software

• AK-MCS

Cites Work

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