Article information

2020 , Volume 25, ¹ 6, p.62-84

Abdrazakova A.R., Voytishek A.V.

Computable simulated transformations of the Cartesian coordinates for random vectors

The purpose of the paper was to expand the range of efficient (economical) computer algorithms for simulation of multi-dimensional random variables.

The authors noticed that in a number of applied problems (for example, when modelling twoor three-dimensional isotropic vectors), transitions from Cartesian to other coordinate systems (for example, to polar or spherical) are effective.

In this regard, the new generalizing notation of the computable simulated transformation of Cartesian coordinates is introduced in the paper. Such transformations allow constructing effective (economical) algorithms for the numerical modelling (simulation) of multi-dimensional random variables.

The problem of finding examples of constructive and practical applications for computer simulated transformations of Cartesian coordinates is formulated.

Transitions to polar, spherical, parabolic and cylindrical coordinates are considered as illustrative examples of such transformations. The practical applications of computable simulated transformations of Cartesian coordinates found in the scientific literature are described in detail by the authors. These applications are associated with both computer modelling (simulation) of isotropic vectors and Gaussian distribution along with the numerical solution of boundary value problems and problems of radiation transfer. Thus, the introduced notion of the computable simulated transformations of Cartesian coordinates is quite constructive. It opens up prospects for the scientific search for new transformations of this type with the aim of using them in stochastic computer models for important processes and phenomena.

[full text] [link to elibrary.ru]

Keywords: numerical simulation of multi-dimensional distributions, transformations of random vectors, computable simulated transformation of the Cartesian coordinates

doi: 10.25743/ICT.2020.25.6.004

Author(s):
Abdrazakova Angela Romanovna
Position: Student
Office: Novosibirsk state university
Address: 630090, Russia, Novosibirsk, 2, Pirogova str.
E-mail: abdrazakova96@inbox.ru

Voytishek Anton Vaclavovich
Dr. , Professor
Position: Leading research officer
Office: Institute of Numerical Mathematics and Mathematical Geophysics of Siberian Division of RAS
Address: 630090, Russia, Novosibirsk, prospect Akademika Lavrentyeva, 6
Phone Office: (383)3307721
E-mail: vav@osmf.sscc.ru
SPIN-code: 7494-4885

References:
1. Mikhailov G.A., Voytishek A.V. Chislennoe statisticheskoe modelirovanie. Metody Monte-Karlo [Numerical statistical modelling. Monte Carlo methods]. Moscow: Youright; 2018: 371. (In Russ.)

2. Box G.E.P., Muller M.E. A note on the generation of random normal deviates. The Annals of Mathematical Statistics. 1958; 29(2):610–611.

3. Fichtenholts G.M. Kurs differentsial’nogo i integral’nogo ischisleniya. T. 3 [Course of differential and integral analysis]. Moscow: Fizmatlit; 2001: 662. (In Russ.)

4. Granshteyn I.S., Ryzhik I.M. Tablitsy integralov, summ, ryadov i proizvedeniy [Tables of integrals, sums, series and products]. Moscow: Gos. izdatel’stvo fiz.-matem. literatury; 1963: 1100. (In Russ.)

5. Mikhailov G.A. About efficient algorithms of numerical-statistical simulation. Numerical Analysis and Applications. 2014; 17(2):147–158.

6. Marchuk G.I., Mikhailov G.A., Nazaraliev M.A., Darbinyan R.A., Kargin B.A., Elepov B.S. Metod Monte-Karlo v atmosfernoy optike [Monte Carlo method in atmospheric optics]. Novosibirsk: Nauka; 1976: 283. (In Russ.)

7. Sabelfeld K.K. Metody Monte-Karlo v kraevykh zadachakh [Monte Carlo methods in boundary value problems]. Novosibirsk: Nauka; 1989: 280. (In Russ.)

8. Ermakov S.M., Sipin A.S. Metod Monte-Karlo i parametricheskaya razdelimost’ algoritmov [Monte Carlo method and parametric separability of algorithms]. SPb.: Izdatel’stvo SPGU; 2014: 248. (In Russ.)

9. Sabelfeld K.K. Random walk on spheres method for solving drift — diffusion problems. Monte Carlo Methods and Applications. 2016; 22(4):265–275.


Bibliography link:
Abdrazakova A.R., Voytishek A.V. Computable simulated transformations of the Cartesian coordinates for random vectors // Computational technologies. 2020. V. 25. ¹ 6. P. 62-84
Home| Scope| Editorial Board| Content| Search| Subscription| Rules| Contacts
ISSN 1560-7534
© 2024 FRC ICT