Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

Jinchao Feng, Joshua Lansford, Alexander Mironenko, Davood Babaei Pourkargar, Dionisios G. Vlachos, Markos A. Katsoulakis

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number035021
JournalAIP Advances
Volume8
Issue number3
DOIs
StatePublished - Mar 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model'. Together they form a unique fingerprint.

Cite this