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Tzvetan Ostromsky
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2020 – today
- 2024
- [c41]Tzvetan Ostromsky, Silvi-Maria Gurova, Meglena D. Lazarova:
Sensitivity Analysis in Air Pollution Modeling Supported by High Performance Supercomputers. FedCSIS (Communication Papers) 2024: 125-130 - 2023
- [c40]Tzvetan Ostromsky, Ivan Dimov, Rayna Georgieva, Venelin Todorov:
Sensitivity Study of a Large-scale Air Pollution Model on the Bulgarian Petascale Supercomputer Discoverer. FedCSIS 2023: 1093-1100 - [c39]Venelin Todorov
, Slavi G. Georgiev
A Stochastic Optimization Technique for UNI-DEM framework. FedCSIS 2023: 1167-1172 - [c38]Venelin Todorov
Improved Stochastic Lattice Methods for Large-Scale Air Pollution Model. LSSC 2023: 363-371 - 2022
- [c37]Tzvetan Ostromsky:
Performance and Scalability Experiments with a Large-scale Air Pollution Model on the EuroHPC Petascale Supercomputer DISCOVERER. FedCSIS (Communication Papers) 2022: 81-84 - [c36]Venelin Todorov, Ivan Dimov, Stefka Fidanova, Rayna Georgieva, Tzvetan Ostromsky
An Optimized Monte Carlo Approach for Multidimensional Integrals Related to Intelligent Systems. FedCSIS (Communication Papers) 2022: 101-104 - [c35]Tzvetan Ostromsky, Venelin Todorov
Sensitivity Analysis of an Air Pollution Model with Using Innovative Monte Carlo Methods in Calculating Multidimensional Integrals. NMA 2022: 258-270 - [c34]Venelin Todorov
Optimized Stochastic Approaches Based on Sobol Quasirandom Sequences for Fredholm Integral Equations of the Second Kind. NMA 2022: 302-313 - [c33]Tzvetan Ostromsky:
Optimization, Performance and Scalability Experiments of a Large Air Pollution Model by Using the EuroHPC Petascale Supercomputer DISCOVERER. WCO 2022: 141-151 - 2021
- [j9]Venelin Todorov
Advanced stochastic approaches for Sobol' sensitivity indices evaluation. Neural Comput. Appl. 33(6): 1999-2014 (2021) - [c32]Venelin Todorov, Tzvetan Ostromsky
Optimized stochastic methods for sensitivity analysis for large-scale air pollution model. FedCSIS (Position Papers) 2021: 85-88 - [c31]Tzvetan Ostromsky
Sensitivity Study of Large-Scale Air Pollution Model Based on Modifications of the Latin Hypercube Sampling Method. LSSC 2021: 156-163 - [c30]Venelin Todorov, Ivan Dimov, Rayna Georgieva
Multidimensional Sensitivity Analysis of an Air Pollution Model Based on Modifications of the van der Corput Sequence. LSSC 2021: 180-187 - 2020
- [c29]Venelin Todorov, Tzvetan Ostromsky
Optimized Quasi-Monte Carlo Method Based on Low Discrepancy Sequences for Sensitivity Analysis in Air Pollution Modelling. FedCSIS (Communication Papers) 2020: 25-28 - [c28]Venelin Todorov, Tzvetan Ostromsky
Sensitivity Study of a Large-Scale Air Pollution Model by Using Optimized Stochastic Algorithm. FedCSIS (Communication Papers) 2020: 29-32 - [c27]Venelin Todorov, Ivan Dimov, Tzvetan Ostromsky
Sensitivity Study of a Large-Scale Air Pollution Model by Using Optimized Latin Hyprecube Sampling. WCO@FedCSIS 2020: 371-387 - [c26]Venelin Todorov, Ivan Dimov, Tzvetan Ostromsky
Optimized Quasi-Monte Carlo Methods Based on Van der Corput Sequence for Sensitivity Analysis in Air Pollution Modelling. WCO@FedCSIS 2020: 389-405
2010 – 2019
- 2019
- [c25]Venelin Todorov
Advanced Quasi-Monte Carlo Algorithms for Multidimensional Integrals in Air Pollution Modelling. HPC 2019: 155-167 - [c24]Tzvetan Ostromsky
Sensitivity Studies of an Air Pollution Model by Using Efficient Stochastic Algorithms for Multidimensional Numerical Integration. HPC 2019: 184-195 - [c23]Tzvetan Ostromsky
Efficient Stochastic Algorithms for the Sensitivity Analysis Problem in the Air Pollution Modelling. LSSC 2019: 420-428 - 2018
- [c22]Tzvetan Ostromsky
Sensitivity Analysis of an Air Pollution Model by Using Quasi-Monte Carlo Algorithms for Multidimensional Numerical Integration. NMA 2018: 281-289 - 2016
- [c21]Tzvetan Ostromsky
On the Performance, Scalability and Sensitivity Analysis of a Large Air Pollution Model. ICCS 2016: 2053-2061 - 2015
- [j8]Tzvetan Ostromsky
Preparing input data for sensitivity analysis of an air pollution model by using high-performance supercomputers and algorithms. Comput. Math. Appl. 70(11): 2773-2782 (2015) - 2014
- [j7]Zahari Zlatev
Application of Richardson extrapolation for multi-dimensional advection equations. Comput. Math. Appl. 67(12): 2279-2293 (2014) - 2013
- [j6]Ivan Dimov, Rayna Georgieva
Advanced algorithms for multidimensional sensitivity studies of large-scale air pollution models based on Sobol sequences. Comput. Math. Appl. 65(3): 338-351 (2013) - 2012
- [j5]Ivan Dimov, Rayna Georgieva
Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model. Reliab. Eng. Syst. Saf. 107: 23-28 (2012) - [c20]Ivan Dimov, Rayna Georgieva
Variance-Based Sensitivity Analysis of the Unified Danish Eulerian Model According to Variations of Chemical Rates. NAA 2012: 247-254 - [c19]Tzvetan Ostromsky
High Performance Computing of Data for a New Sensitivity Analysis Algorithm, Applied in an Air Pollution Model. NAA 2012: 428-436 - 2011
- [c18]Krassimir Georgiev, Tzvetan Ostromsky
New Parallel Implementation of an Air Pollution Computer Model - Performance Study on an IBM Blue Gene/P Computer. LSSC 2011: 283-290 - [c17]Tzvetan Ostromsky
Parallel Computation of Sensitivity Analysis Data for the Danish Eulerian Model. LSSC 2011: 307-315 - 2010
- [j4]Ivan Dimov, Rayna Georgieva
Studying the sensitivity of pollutants' concentrations caused by variations of chemical rates. J. Comput. Appl. Math. 235(2): 391-402 (2010) - [c16]Zahari Zlatev
Richardson Extrapolated Numerical Methods for Treatment of One-Dimensional Advection Equations. NMA 2010: 198-206
2000 – 2009
- 2009
- [c15]Tzvetan Ostromsky
Sensitivity Analysis of a Large-Scale Air Pollution Model: Numerical Aspects and a Highly Parallel Implementation. LSSC 2009: 197-205 - 2008
- [c14]Tzvetan Ostromsky
Parallel Performance and Scalability Experiments with the Danish Eulerian Model on the EPCC Supercomputers. NAA 2008: 447-453 - 2007
- [j3]Petra Csomós
Computational complexity of weighted splitting schemes on parallel computers. Int. J. Parallel Emergent Distributed Syst. 22(3): 137-147 (2007) - 2006
- [c13]Tzvetan Ostromsky
Parallel and GRID Implementation of a Large Scale Air Pollution Model. Numerical Methods and Applications 2006: 475-482 - 2004
- [c12]Tzvetan Ostromsky
Parallel Implementation and One Year Experiments with the Danish Eulerian Model. NAA 2004: 440-447 - 2002
- [c11]Tzvetan Ostromsky
Flexible Two-Level Parallel Implementations of a Large Air Pollution Model. Numerical Methods and Application 2002: 545-554 - 2001
- [c10]Tzvetan Ostromsky
Computational challenges in large-scale air pollution modelling. ICS 2001: 407-418 - [c9]Tzvetan Ostromsky
Parallel Implementation of a Large-Scale 3-D Air Pollution Model. LSSC 2001: 309-316 - 2000
- [c8]Tzvetan Ostromsky
Estimation of the Wheat Losses Caused by the Tropospheric Ozone in Bulgaria and Denmark. NAA 2000: 636-643
1990 – 1999
- 1999
- [c7]Ivan Dimov, Tzvetan Ostromsky, Ilia Tzvetanov, Zahari Zlatev:
Economical Estimation of the Losses of Crops Due to High Ozone Levels. LSSC 1999: 275-282 - 1998
- [j2]Tzvetan Ostromsky
A Coarse-Grained Parallel QR-Factorization Algorithm for Sparse Least Squares Problems. Parallel Comput. 24(5-6): 937-964 (1998) - 1997
- [c6]Tzvetan Ostromsky, Zahari Zlatev:
Application of Sparse Matrix Techniques in the Chemical Part of a Large Air Pollution Model. LSSC 1997: 189-200 - 1996
- [c5]Tzvetan Ostromsky
Using Dense Matrix Computations in the Solution of Sparse Problems. WNAA 1996: 357-364 - [c4]Tzvetan Ostromsky
Parallel Solution of Sparse Problems by Using a Sequence of Large Dense Blocks. PARA 1996: 555-564 - 1995
- [j1]Kyle A. Gallivan, Per Christian Hansen, Tzvetan Ostromsky
Locally Optimized Reordering Algorithm and its Application to a Parallel Sparse Linear System Solver. Computing 54(1): 39-68 (1995) - [c3]Tzvetan Ostromsky
A Parallel Sparse QR-Factorization Algorithm. PARA 1995: 462-472 - 1994
- [c2]Zdravko Karakehayov, Tzvetan Ostromsky:
Distributed Control using Shared Input. Applied Informatics 1994: 276-278 - [c1]Per Christian Hansen, Tzvetan Ostromsky
Two Enhancements in a Partitioned Sparse Code. PARA 1994: 296-303
Coauthor Index
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