Stochastic galerkin and collocation methods for quantifying uncertainty in differential equations: a review

  • Jakeman J
  • Roberts S
19Citations
Citations of this article
36Readers
Mendeley users who have this article in their library.

Abstract

The article reviews the mathematical theory of stochastic Galerkin and stochastic collocation methods, focusing on their strengths and limitations. The aim is to construct a first stop, widely accessible document that directs a reader to more detailed descriptions of stochastic Galerkin and stochastic collocation methods that are suitable for their application of interest. References point to rigorous convergence proofs and accuracy estimates, computational considerations and numerical examples. A supplementary document gives a quick look-up guide to the strengths and weaknesses of stochastic Galerkin and collocation methods. © Austral. Mathematical Soc. 2009.

References Powered by Scopus

The Wiener-Askey polynomial chaos for stochastic differential equations

4269Citations
N/AReaders
Get full text

High-order collocation methods for differential equations with random inputs

1342Citations
N/AReaders
Get full text

Modeling uncertainty in flow simulations via generalized polynomial chaos

1337Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A concept for data-driven uncertainty quantification and its application to carbon dioxide storage in geological formations

98Citations
N/AReaders
Get full text

An integrative approach to robust design and probabilistic risk assessment for CO<inf>2</inf> storage in geological formations

67Citations
N/AReaders
Get full text

Global sensitivity analysis: A flexible and efficient framework with an example from stochastic hydrogeology

67Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Jakeman, J. D., & Roberts, S. G. (2009). Stochastic galerkin and collocation methods for quantifying uncertainty in differential equations: a review. ANZIAM Journal, 50, 815. https://doi.org/10.21914/anziamj.v50i0.1410

Readers over time

‘10‘11‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘2502468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 16

52%

Researcher 12

39%

Professor / Associate Prof. 3

10%

Readers' Discipline

Tooltip

Mathematics 14

50%

Engineering 11

39%

Agricultural and Biological Sciences 2

7%

Design 1

4%

Save time finding and organizing research with Mendeley

Sign up for free
0