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Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation. / Lotova, G. Z.; Mikhailov, G. A.; Rogasinsky, S. V.

In: Computational Mathematics and Mathematical Physics, Vol. 64, No. 5, 05.2024, p. 1065-1075.

Research output: Contribution to journal › Article › peer-review

Harvard

Lotova, GZ, Mikhailov, GA & Rogasinsky, SV 2024, 'Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation', Computational Mathematics and Mathematical Physics, vol. 64, no. 5, pp. 1065-1075. https://doi.org/10.1134/S0965542524700246

APA

Lotova, G. Z., Mikhailov, G. A., & Rogasinsky, S. V. (2024). Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation. Computational Mathematics and Mathematical Physics, 64(5), 1065-1075. https://doi.org/10.1134/S0965542524700246

Vancouver

Lotova GZ, Mikhailov GA, Rogasinsky SV. Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation. Computational Mathematics and Mathematical Physics. 2024 May;64(5):1065-1075. doi: 10.1134/S0965542524700246

Author

Lotova, G. Z. ; Mikhailov, G. A. ; Rogasinsky, S. V. / Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation. In: Computational Mathematics and Mathematical Physics. 2024 ; Vol. 64, No. 5. pp. 1065-1075.

BibTeX

@article{db3fc76617994d3988baf02a7e394455,
title = "Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation",
abstract = "Abstract: The main goal of this work is to check the hypothesis that the well-known N-particle statistical algorithm yields a solution estimate for the nonlinear Boltzmann equation with an error. For this purpose, practically important optimal relations between and the number of sample estimate values are determined. Numerical results for a problem with a known solution confirm that the formulated estimates and conclusions are satisfactory.",
keywords = "Boltzmann equation, Monte Carlo method, N-particle Markov chain, majorizing frequency method, molecular chaos, statistical modeling",
author = "Lotova, {G. Z.} and Mikhailov, {G. A.} and Rogasinsky, {S. V.}",
year = "2024",
month = may,
doi = "10.1134/S0965542524700246",
language = "English",
volume = "64",
pages = "1065--1075",
journal = "Computational Mathematics and Mathematical Physics",
issn = "0965-5425",
publisher = "PLEIADES PUBLISHING INC",
number = "5",

}

RIS

TY - JOUR

T1 - Study and Optimization of N-Particle Numerical Statistical Algorithm for Solving the Boltzmann Equation

AU - Lotova, G. Z.

AU - Mikhailov, G. A.

AU - Rogasinsky, S. V.

PY - 2024/5

Y1 - 2024/5

N2 - Abstract: The main goal of this work is to check the hypothesis that the well-known N-particle statistical algorithm yields a solution estimate for the nonlinear Boltzmann equation with an error. For this purpose, practically important optimal relations between and the number of sample estimate values are determined. Numerical results for a problem with a known solution confirm that the formulated estimates and conclusions are satisfactory.

AB - Abstract: The main goal of this work is to check the hypothesis that the well-known N-particle statistical algorithm yields a solution estimate for the nonlinear Boltzmann equation with an error. For this purpose, practically important optimal relations between and the number of sample estimate values are determined. Numerical results for a problem with a known solution confirm that the formulated estimates and conclusions are satisfactory.

KW - Boltzmann equation

KW - Monte Carlo method

KW - N-particle Markov chain

KW - majorizing frequency method

KW - molecular chaos

KW - statistical modeling

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85196121994&origin=inward&txGid=be3cc2aa598e39343b239aa38f8b0c72

UR - https://www.mendeley.com/catalogue/a4e8d3d9-1000-34f4-ab0f-281d9ba67353/

U2 - 10.1134/S0965542524700246

DO - 10.1134/S0965542524700246

M3 - Article

VL - 64

SP - 1065

EP - 1075

JO - Computational Mathematics and Mathematical Physics

JF - Computational Mathematics and Mathematical Physics

SN - 0965-5425

IS - 5

ER -

ID: 61123592