Siberian Federal University 
- Issue
- Journal of Siberian Federal University. Mathematics & Physics. Prepublication
- Authors
- Dobronets, Boris S.; Popova, Olga A.
- Contact information
- Dobronets, Boris S.: Siberian Federal University (Krasnoyarsk, Russian Federation); OCRID: 0000-0002-0167-1637;; Popova, Olga A.: Siberian Federal University (Krasnoyarsk, Russian Federation); OCRID: 0000-0002-5739-2741
- Keywords
- computational probabilistic analysis; probabilistic extensions; uncertainty quantification
- Abstract
The article studies fast algorithms for numerical modeling of problems with random input data. The approaches under consideration are non-intrusive methods based on probabilistic extensions. They rely on existing numerical methods for solving deterministic problems and use them as solvers. Unlike polynomial chaos methods, in some cases it is possible to avoid exponential growth of the number of operations from the number of input parameters
- Pages
- 291–299
- EDN
- BPCWON
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/158229
Journal of Siberian Federal University. Mathematics & Physics / Fast Numerical Methods for Stochastic Modeling Based on Probabilistic Extensions
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