Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model
Abstract
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2018
- Source ID
- 10.1063/1.5021351
Entities
People
- Alexander Mironenko
- Davood Babaei Pourkargar
- Dionisios G. Vlachos
- Jinchao Feng
- Joshua L Lansford
- Markos A Katsoulakis
Organizations
- Defense Advanced Research Projects Agency
- University of Delaware
- University of Massachusetts Amherst