Home Numerical study of the coefficient identification algorithm based on ensembles of adjoint problem solutions for a production-destruction model
Article
Licensed
Unlicensed Requires Authentication

Numerical study of the coefficient identification algorithm based on ensembles of adjoint problem solutions for a production-destruction model

  • Alexey V. Penenko ORCID logo EMAIL logo , Zhadyra S. Mukatova and Akzhan B. Salimova
Published/Copyright: September 29, 2020

Abstract

A numerical algorithm for the solution of an inverse coefficient problem for nonstationary, nonlinear production-destruction type model is proposed and tested on an example of the Lorenz’63 system. With an ensemble of adjoint problem solutions, the inverse problem is transformed into a quasi-linear matrix problem and solved with Newton-type algorithm. Two different ways of the adjoint ensemble construction are compared. In the first one, a trigonometric basis is used. In the second one in situ measurements are taken into account. Local convergence properties of the algorithm are studied numerically to find out when the use of more data can lead to the degradation of the reconstruction results.

MSC: 47J06; 65N21

Corresponding author: Alexey V. Penenko, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russian Federation; Novosibirsk State University, Novosibirsk, Russian Federation, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The work was supported by RSF project 17-71-10184 in the part of the algorithms development with Fourier-type data reduction and by RFBR project 19-07-01135 in the part of the algorithms development and numerical analysis for inverse coefficient problems with point-wise data reduction.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] H. Zhang, J. C. Linford, A. Sandu, and R. Sander, “Chemical mechanism solvers in air quality models,” Atmosphere, vol. 2, no. 3, pp. 510–532, 2011, https://doi.org/10.3390/atmos2030510.10.3390/atmos2030510Search in Google Scholar

[2] E. N. Lorenz, “Deterministic nonperiodic flow,” J. Atmos. Sci., vol. 20, no. 2, pp. 130–141, Mar 1963, https://doi.org/10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2.10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2Search in Google Scholar

[3] I. Boussaid, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,” Inf. Sci., vol. 237, pp. 82–117, Jul 2013, https://doi.org/10.1016/j.ins.2013.02.041.10.1016/j.ins.2013.02.041Search in Google Scholar

[4] G. Marchuk, “On the formulation of some inverse problems. Reports of the USSR Academy of Sciences / Ed,” Science, vol. 156, no. 3, pp. 503–506, 1964. (In Russian), https://doi.org/10.1007/bf01462264.10.1007/BF01462264Search in Google Scholar

[5] J. P. Issartel, “Rebuilding sources of linear tracers after atmospheric concentration measurements,” Atmos. Chem. Phys., vol. 3, no. 6, pp. 2111–2125, Dec 2003, https://doi.org/10.5194/acp-3-2111-2003.10.5194/acp-3-2111-2003Search in Google Scholar

[6] J. P. Issartel, “Emergence of a tracer source from air concentration measurements, a new strategy for linear assimilation,” Atmos. Chem. Phys., vol. 5, no. 1, pp. 249–273, Feb 2005, https://doi.org/10.5194/acp-5-249-2005.10.5194/acp-5-249-2005Search in Google Scholar

[7] G. Turbelin, S. K. Singh, and J. P. Issartel, “Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique,” J. Adv. Model. Earth Syst., vol. 6, no. 4, pp. 1244–1255, Dec 2014, https://doi.org/10.1002/2014ms000385.10.1002/2014MS000385Search in Google Scholar

[8] A. V. Penenko, “Consistent numerical schemes for solving nonlinear inverse source problems with gradient-type algorithms and Newton-Kantorovich methods,” Numer. Anal. Appl., vol. 11, no. 1, pp. 73–88, Jan 2018, https://doi.org/10.1134/s1995423918010081.10.1134/S1995423918010081Search in Google Scholar

[9] A. Penenko, “A Newton–Kantorovich method in inverse source problems for production-destruction models with time series-type measurement data,” Numer. Anal. Appl., vol. 12, no. 1, pp. 51–69, Jan 2019. https://doi.org/10.1134/S1995423919010051.10.1134/S1995423919010051Search in Google Scholar

[10] A. Penenko, U. Zubairova, Z. Mukatova, and S. Nikolaev, “Numerical algorithm for morphogen synthesis region identification with indirect image-type measurement data,” J. Bioinf. Comput. Biol., vol. 17, pp. 1940002-1–1940002-18, Jan 2019. https://doi.org/10.1142/s021972001940002x.10.1142/S021972001940002XSearch in Google Scholar

[11] A. Penenko, S. Nikolaev, S. Golushko, A. Romashenko, and I. Kirilova, “Numerical algorithms for diffusion coefficient identification in problems of tissue engineering,” Math. Biol. Bioinf., vol. 11, no. 2, pp. 426–444, 2016. (In Russian), https://doi.org/10.17537/2016.11.426.10.17537/2016.11.426Search in Google Scholar

[12] A. L. Karchevsky, “Reformulation of an inverse problem statement that reduces computational costs,” Eurasian Int. J. Comput. Math. Sci. Appl., vol. 1, no. 2, pp. 4–20, 2013, https://doi.org/10.32523/2306-3172-2013-1-2-4-20.10.32523/2306-3172-2013-1-2-4-20Search in Google Scholar

[13] A. F. Bennett. Inverse Methods in Physical Oceanography (Cambridge Monographs on Mechanics), Cambridge University Press, 1992.10.1017/CBO9780511600807Search in Google Scholar

[14] M. A. Iglesias and C. Dawson, “An iterative representer-based scheme for data inversion in reservoir modeling,” Inverse Probl., vol. 25, no. 3, pp. 1–34, Jan 2009, https://doi.org/10.1088/0266-5611/25/3/035006.10.1088/0266-5611/25/3/035006Search in Google Scholar

[15] V. Penenko, A. Baklanov, and E. Tsvetova, “Methods of sensitivity theory and inverse modeling for estimation of source parameters,” Future Generat. Comput. Syst., vol. 18, no. 5, pp. 661–671, Apr 2002, https://doi.org/10.1016/s0167-739x(02)00031-6.10.1016/S0167-739X(02)00031-6Search in Google Scholar

[16] P. E. Bieringer, G. S. Young, L. M. Rodriguez, A. J. Annunzio, F. Vandenberghe, and S. E. Haupt, “Paradigms and commonalities in atmospheric source term estimation methods,” Atmos. Environ., vol. 156, pp. 102–112, May 2017, https://doi.org/10.1016/j.atmosenv.2017.02.011.10.1016/j.atmosenv.2017.02.011Search in Google Scholar

[17] D. Poland, “Cooperative catalysis and chemical chaos: a chemical model for the Lorenz equations,” Phys. D: Nonlinear Phenomen., vol. 65, no. 1-2, pp. 86–99, May 1993, https://doi.org/10.1016/0167-2789(93)90006-m.10.1016/0167-2789(93)90006-MSearch in Google Scholar

[18] G. Evensen and N. Fario, “Solving for the generalized inverse of the Lorenz model,” J. Meteorol. Soc. Jpn., vol. 75, no. 1B, pp. 229–243, 1997, https://doi.org/10.2151/jmsj1965.75.1b_229.10.2151/jmsj1965.75.1B_229Search in Google Scholar

[19] J. Lu, J. Lu, J. Xie, and G. Chen, “Reconstruction of the lorenz and chen systems with noisy observations,” Comput. Math. Appl., vol. 46, no. 8-9, pp. 1427–1434, Oct 2003, https://doi.org/10.1016/s0898-1221(03)90230-6.10.1016/S0898-1221(03)90230-6Search in Google Scholar

[20] B. Ahrens, “Variational data assimilation for a Lorenz model using a non-standard genetic algorithm,” Meteorol. Atmos. Phys., vol. 70, no. 3, pp. 227–238, 1999, https://doi.org/10.1007/s007030050036.10.1007/s007030050036Search in Google Scholar

[21] V. V. Penenko, “Some aspects of mathematical modelling using the models together with observational data,” Bull. Nov. Comp. Center, Series Num. Model. Atmosph., vol. 4, pp. 31–52, 1996.Search in Google Scholar

Received: 2019-03-14
Accepted: 2020-06-01
Published Online: 2020-09-29
Published in Print: 2021-08-26

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 26.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijnsns-2019-0088/html
Scroll to top button