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

By Sergey Oladyshkin, F. D. Barros, Wolfgang Nowak. 2012

75 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/99bbd9ffce05c15f74405166d9439f365b798899

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. B. Ataie‐Ashtiani
2. Craig T. Simmons
3. M. Rajabi
4. Sergey Oladyshkin
5. Wolfgang Nowak
6. Holger Class

In Environmental Engineering

1. Sergey Oladyshkin
2. Wolfgang Nowak
3. Holger Class
4. B. Ataie‐Ashtiani
5. Craig T. Simmons
6. M. Rajabi
7. F. P. J. D. Barros
8. Yoram Rubin

In Statistics and Probability

1. Wolfgang Nowak
2. Sergey Oladyshkin
3. Holger Class
4. B. Ataie‐Ashtiani
5. Craig T. Simmons
6. M. Rajabi

Citations in the corpus

• Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, background): “, [8, 59, 72]) or regularization choices, is very important.”
• Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012, background): “The papers [42] [45] demonstrate correspondingly how classical PCE and its new aPC version can deliver the information required for global sensitivity analysis at low computational costs.”
• Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, background): “A number of more recent studies in groundwater modeling have employed derivative-based (e.g., Malaguerra et al., 2013) and variance-based (e.g., Oladyshkin et al., 2012) global SA methods.”