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Advanced Stochastic Approaches for Applied Computing in Environmental Modeling

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Parallel Processing and Applied Mathematics (PPAM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13826))

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Abstract

Mathematical models are used to study and predict the behavior of a variety of complex systems - engineering, physical, economic, social, environmental. Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of applications. Sensitivity analysis is a modern promising technique for studying large-scale systems such as ecological systems. The uncertainty in the model input in our case, as in many others, can be due to various reasons: inaccurate measurements or calculation, approximation, data compression, etc. Two kinds of sensitivity analysis have been discussed in the literature: local and global. In the current paper the subject of our study is the global sensitivity analysis performed via the Sobol’ variance-based approach, applied to a specific large-scale air pollution model. The mathematical treatment of the problem of providing global sensitivity analysis consists in evaluating total sensitivity indices which leads to computing multidimensional integrals. We propose a new specific stochastic approach which significantly improves the results by the standard stochastic approaches.

Venelin Todorov is supported by the Bulgarian National Science Fund under Project KP-06-N52/5 “Efficient methods for modeling, optimization and decision making” and KP-06-M32/2 - 17.12.2019 “Advanced Stochastic and Deterministic Approaches for Large-Scale Problems of Computational Mathematics”. The work is also supported by the BNSF under Projects KP-06-N52/5 “Efficient methods for modeling, optimization and decision making” and Bilateral Project KP-06-Russia/17 “New Highly Efficient Stochastic Simulation Methods and Applications”.

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Acknowledgements

Venelin Todorov is supported by the Bulgarian National Science Fund (BNSF) under Project KP-06-N52/5 “Efficient methods for modeling, optimization and decision making” and BNSF under Project KP-06-N62/6 “Machine learning through physics-informed neural networks”. The work is also supported by BNSF under Project KP-06-M32/2-17.12.2019 “Advanced Stochastic and Deterministic Approaches for Large-Scale Problems of Computational Mathematics” and Bilateral Project KP-06-Russia/17 “New Highly Efficient Stochastic Simulation Methods and Applications”.

Author information

Authors and Affiliations

  1. Department of Information Modeling, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Street, Block 8, 1113, Sofia, Bulgaria

    Venelin Todorov

  2. Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Block 25 A, 1113, Sofia, Bulgaria

    Venelin Todorov & Ivan Dimov

  3. Polish Academy of Sciences, Warszawa, Poland

    Maria Ganzha & Marcin Paprzycki

Authors
  1. Venelin Todorov
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  2. Ivan Dimov
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  3. Maria Ganzha
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  4. Marcin Paprzycki
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Corresponding author

Correspondence to Venelin Todorov .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, TN, USA

    Jack Dongarra

  3. University of Southern California, Marina del Rey, CA, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Todorov, V., Dimov, I., Ganzha, M., Paprzycki, M. (2023). Advanced Stochastic Approaches for Applied Computing in Environmental Modeling. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2022. Lecture Notes in Computer Science, vol 13826. Springer, Cham. https://doi.org/10.1007/978-3-031-30442-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-30442-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30441-5

  • Online ISBN: 978-3-031-30442-2

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