FAST AND FLEXIBLE UNCERTAINTY QUANTIFICATION THROUGH A DATA-DRIVEN SURROGATE MODEL

Subjects

Subjects :

Statistics and Probability
Control and Optimization
Dynamical systems theory
Computer science
05 social sciences
Monte Carlo method
010501 environmental sciences
01 natural sciences
Data-driven
Surrogate model
Modeling and Simulation
0502 economics and business
Discrete Mathematics and Combinatorics
Uncertainty quantification
Algorithm
050203 business & management
0105 earth and related environmental sciences

Details

ISSN :

21525080

Volume :

8

Database :

OpenAIRE

Journal :

International Journal for Uncertainty Quantification

Accession number :

edsair.doi...........7e3f64b969748409e0d812e294c6a657

Full Text :

https://doi.org/10.1615/int.j.uncertaintyquantification.2018021975