On the Construction of Uncertain Time Series Surrogates Using Polynomial Chaos and Gaussian Processes-Reference-Cited by-同舟云学术

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On the Construction of Uncertain Time Series Surrogates Using Polynomial Chaos and Gaussian Processes

Published:2019-05-20 Issue:2 Volume:52 Page:285-309
ISSN:1874-8961
Container-title:Mathematical Geosciences
language:en
Short-container-title:Math Geosci

Author:

Sochala Pierre,Iskandarani Mohamed

Funder

National Aeronautics and Space Administration

National Science Foundation

Association Instituts Carnot

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,Mathematics (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s11004-019-09806-8.pdf

Reference43 articles.

1. Alemazkoor N, Meidani H (2017) Divide and conquer: an incremental sparsity promoting compressive sampling approach for polynomial chaos expansions. Comput Methods Appl Mech Eng 318:937–956

2. Alexanderian A, Le Maître O, Najm H, Iskandarani M, Knio O (2012) Multiscale stochastic preconditioners in non-intrusive spectral projection. SIAM J Sci Comput 50(2):306–340

3. Blatman G, Sudret B (2011) Adaptive sparse polynomial chaos expansion based on least angle regression. J Comput Phys 230(6):2345–2367

4. Bleck R (2002) An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates. Ocean Model 4(1):55–88

5. Bowman A, Azzalini A (1997) Applied smoothing techniques for data analysis. Oxford University Press, New York

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