Rayna Georgieva

Dr. Rayna Georgieva

Institute of Information and Communication Technologies, Sofia, Sofia-Grad, Bulgaria
orcid

Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2010 in Mathematics and Computers in Simulation
Verified
Journal Article
Closed access
IDI. Dimov
I. Dimov
RGRayna Georgieva
Rayna Georgieva

1 February 2013 in Computers & Mathematics with Applications
Verified
Journal Article
Closed access
IDI. Dimov
I. Dimov
RGRayna Georgieva
Rayna Georgieva
TOTz. Ostromsky
Tz. Ostromsky
ZZZahari Zlatev
Zahari Zlatev

1 January 2011
Verified
Book Chapter
Closed access
PDF file
IDIvan Dimov
Ivan Dimov
RGRayna Georgieva
Rayna Georgieva


21 June 2020 in Neural Computing and Applications
Verified
Journal Article
Closed access
PDF file
Venelin TodorovVenelin Todorov
Venelin Todorov
Ivan DimovIvan Dimov
Ivan Dimov
TOTzvetan Ostromsky
Tzvetan Ostromsky
SAStoyan Apostolov
Stoyan Apostolov
RGRayna Georgieva
Rayna Georgieva
ZZZahari Zlatev
Zahari Zlatev

1 January 2013 in Open Mathematics
Verified
Journal Article
Open access
PDF file
Ivan DimovIvan Dimov
Ivan Dimov
RGRayna Georgieva
Rayna Georgieva
TOTzvetan Ostromsky
Tzvetan Ostromsky
ZZZahari Zlatev
Zahari Zlatev
The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) with different geographical locations is considered. Sensitivity analysis of the output of the Unified Danish Eulerian Model according to emission levels is provided. The Sobol’ variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. To measure the influence of the variation of emission levels over the pollutants concentrations the Sobol’ global sensitivity indices are estimated using efficient techniques for small sensitivity indices to avoid the effect of loss of accuracy. Theoretical studies, as well as, practical computations are performed in order to analyze efficiency of various variance reduction techniques for computing small indices. The importance of accurate estimation of small sensitivity indices is analyzed. It is shown that the correlated sampling technique for small sensitivity indices gives reliable results for the full set of indices. Its superior efficiency is studied in details.