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20PLenary session 19
moment, we have provided testing calculations using simulated data. Now we are testing our approach on
real data.
References:
1. Y. Wang, D. Lukyanenko, A. Yagola. Magnetic parameters inversion method with full tensor gradient data //
Inverse Problem and Imaging. 2019. V. 13, no. 4. P. 745.754, DOI. 10.3934/ipi.2019034.
2. Y. Wang, I. Kolotov, D. Lukyanenko, A. Yagola. Reconstruction of Magnetic Susceptibility Using Full
Magnetic Gradient Data June 2020 Computational Mathematics and Mathematical Physics 60(6):1000.1007,
DOI: 10.1134/S096554252006010X.
Adiffusion.convection problem with a fractional derivative alongthe trajectory of motion
A. V. Lapin1, V. V. Shaidurov2
1Sechenov University, Moscow
2Institute of Computational Modeling, Siberian branch of RAS, Krasnoyarsk
Email: [email protected]
DOI 10.24412/cl.35065.2021.1.00.36
A new mathematical modelof the diffusion.convective process with "memory along the flow path" is proposed.
This process is described by a homogeneous one.dimensionalDirichlet problem with a fractional derivative
along the characteristic curve of the convection operator, or, in other words, with fractional material derivative.
A finite.difference scheme is constructed using an analogue of the well.known L1.approximation of
time.fractional derivative for the fractional material derivative and the conventional approximation of the diffusion
term..The unique solvability of the constructed mesh scheme is proved. The stability estimates are derived
in the uniform mesh norm, and the accuracy estimates are given under the assumptions of sufficient
smoothness of the initial data and the solution of the differential problem. The presented results are based on
the article [1].
This work was supported by the Russian Scientific Foundation (grant 20.61.46017).
References
1. Lapin A. V., Shaidurov V.V. A diffusion.convection problem with a fractional derivative along the trajectory of
motion// RJNAMM. 2021. V.36, N.3. P. 157.163.
Numerically statisticalinvestigation of efficacy of SEIR model
G. Z. Lotova1,2, V. L. Lukinov1,2, M. A. Marchenko1,2, G. A. Mikhailov1, D. D. Smirnov1
1Institute of Computational Mathematics and Mathematical Geophysics SB RAS
2Novosibirsk State University
Email: [email protected]
DOI 10.24412/cl.35065.2021.1.00.82
A comparative analysis of the differentialand the corresponding stochastic Poisson SEIR.models [1, 2] was
performed for the testing problem of the epidemic COVID.19 in Novosibirsk modeling in the period from the
23rd of March 2020 to the 21th of June 2020, with the initial population N =2 798 170 [3]. By varying the initial
population in the form N=n�� m with m�.�2, it was shown that the average values of the sickidentified was
less (beginning with the 7th of April2020) the corresponding differential values by the quantity that is statistically
not distinguished from C(t)/m,with C�. 27.3 on the 21th June.This relationship allows to use the stochas
