A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

9 February 2022

Papers citing "A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems"

5 / 5 papers shown

Title
Learning signals defined on graphs with optimal transport and Gaussian process regression Raphael Carpintero Perez Sébastien da Veiga Josselin Garnier B. Staber 30 1 0 21 Oct 2024
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds Andrea Zanoni Gianluca Geraci Matteo Salvador Alison L. Marsden Daniele E. Schiavazzi 13 1 0 07 Aug 2024
Grassmannian diffusion maps based surrogate modeling via geometric harmonics K. R. D. dos Santos Dimitris G. Giovanis Katiana Kontolati Dimitrios Loukrezis Michael D. Shields 17 9 0 28 Sep 2021
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation Somayajulu L. N. Dhulipala Michael D. Shields B. Spencer C. Bolisetti A. Slaughter V. Labouré P. Chakroborty 16 24 0 25 Jun 2021
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 84 271 0 24 Feb 2014