Vestnik КRAUNC. Fiz.-Mat. nauki. 2024. vol. 47. no. 2. P. 35 – 57. ISSN 2079-6641

MATHEMATICAL MODELING
https://doi.org/10.26117/2079-6641-2024-47-2-35-57
Research Article
Full text in Russian
MSC 26A33, 49N45

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The optimization problem for determining the functional dependence of the variable order of the fractional derivative of the Gerasimov-Caputo type

D. A. Tverdyi^\ast, R. I. Parovik

Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034, Paratunka village, Mirnaya str., 7, Russia

Abstract. When solving mathematical modeling problems, it is often necessary to turn to the theory of integral and differential calculus. This theory can be used to describe dynamic processes of various types. The use of fractional derivatives allows us to refine some models by taking into account the memory effect, which is expressed in the equations depending on the current state of the system from previous states. This effect is called non-locality and its intensity is determined by the value of the exponent in the fractional derivative. Classically, this value \alpha a noninteger constant, but there are also generalizations for time-varying nonlocality and other functional dependencies. Fractional differential models are finding increasing application in the physical, mathematical, and technical sciences. However, given the nature of the modeled process, the selection of various parameters for such models must be carried out empirically. Model parameters are refined by iterating through values and comparing simulation results with experimental data representing the process. This process continues until the results begin to qualitatively approximate the data, which is a time-consuming process that inevitably leads to ideas about solving inverse problems. The purpose of this work is to demonstrate that it is possible to use methods of unconditional optimization to solve inverse problems and determine the type of functional dependence \alpha(t). The direct problem is formulated as a Cauchy problem for a fractional differential equation, where the derivative is interpreted in the sense of Gerasimov-Caputo with a variable exponent \alpha(t) for the fractional derivative. The direct problem is solved numerically using a nonlocal, implicit finite difference scheme. The inverse problem is defined as the problem of discrete minimization of the function \alpha(t) based on experimental data. To solve this problem, we have chosen the Levenberg-Marquardt iterative method. Through test examples, we have shown that this method can be used for unconstrained optimization to determine the shape of the function \alpha(t) and its optimal values in various models.

Key words: Inverse problems, non-conditional optimization, Newton methods of function minimization, Levenberg-Marquardt algorithm, fractional derivatives, Gerasimov-Caputo, memory effect, non-locality, implicit finite-difference schemes.

Received: 29.04.2024; Revised: 30.05.2024; Accepted: 09.06.2024; First online: 25.08.2024

For citation. Tverdyi D. A., Parovik R. I. The optimization problem for determining the functional dependence of the variable order of the fractional derivative of the Gerasimov-Caputo type. Vestnik KRAUNC. Fiz.-mat. nauki. 2024, 47:2, 35-57. EDN: PVTXPV. https://doi.org/10.26117/2079-6641-2024-47-2-35-57.

Funding. The research was funded by a grant from the Russian Science Foundation, project number 23-71-01050, which can be found at https://rscf.ru/project/23-71-01050/

Competing interests. There are no conflicts of interest regarding authorship and publication.

Contribution and Responsibility. All authors contributed to this article. Authors are solely responsible for providing the final version of the article in print. The final version of the manuscript was approved by all authors.

^\astCorrespondence: E-mail: tverdyi@ikir.ru

The content is published under the terms of the Creative Commons Attribution 4.0 International License

© Tverdyi D. A., Parovik R. I., 2024

© Institute of Cosmophysical Research and Radio Wave Propagation, 2024 (original layout, design, compilation)

References

  1. Kilbas A. A., Srivastava H. M., Trujillo J. J. Theory and Applications of Fractional Differential Equations. Amsterdam, Elsevier, 2006, 540 pp., isbn: 9780444518323.
  2. Iomin A. Fractional-time quantum dynamics, Physical Review E, 2009, vol. 80, no. 2, pp. 1–4. DOI: 10.1103/PhysRevE.80.022103.
  3. Bagley R. L., Torvik P. J. A theoretical basis for the application of fractional calculus to viscoelasticity, Journal of rheology, 1983, vol. 27, no. 3, pp. 201–210. DOI: 10.1122/1.549724.
  4. Coimbra C. F. M. Mechanics with variable-order differential operators, Annalen der Physik, 2003, vol. 12, no. 11-12, pp. 692–703. DOI: 10.1002/andp.200310032.
  5. Rossikhin Y. A., Shitikova M. V. Application of fractional calculus for dynamic problems of solid mechanics: novel trends and recent results, Applied Mechanics Reviews, 2010, vol. 63, no. 1:010801, pp. 1–52. DOI: 10.1115/1.4000563.
  6. Metzler R., Klafter J. The restaurant at the end of the random walk: recent developments in the description of anomalous transport by fractional dynamics, Journal of Physics A Mathematical and General, 2004, vol. 37, no. 31, pp. 161–208. DOI: 10.1088/0305-4470/37/31/R01.
  7. Moroz L. I., Maslovskaya A. G. Numerical Simulation of an Anomalous Diffusion Process Based on a Scheme of a Higher Order of Accuracy, Mathematical Models and Computer Simulations, 2021, vol. 13, no. 3, pp. 492–501. DOI: 10.1134/S207004822103011X.
  8. Parovik R. I. Mathematical modeling of linear fractional oscillators, Mathematics, 2020, vol. 8, no. 11:1879, pp. 1–26. DOI: 10.3390/math8111879.
  9. Sun H.G., Chen W., Wei H., Chen Y. Q. A comparative study of constant-order and variable-order fractional models in characterizing memory property of systems, The European Physical Journal-Special Topics, 2011, vol. 193, no. 1, pp. 185–192. DOI: 10.1140/epjst/e2011-01390-6.
  10. Volterra V. Sur les ´equations int´egro-diff´erentielles et leurs applications, Acta Mathematica, 1912, vol. 35, no. 1, pp. 295–356. DOI: 10.1007/BF02418820.
  11. Nahushev A. M. Drobnoe ischislenie i ego primenenie [Fractional calculus and its application]. Moscow: Fizmatlit, 2003, 272 pp., isbn: 5-9221-0440-3 (In Russian)
  12. Rekhviashvili S. S., Pskhu A. V. Drobnyj oscillyator s eksponencial’no-stepennoj funkciej pamyati [Fractional oscillator with exponential-power memory function], Pis’ma v ZHTF [Letters to ZhTF], 2022, vol. 48, no. 7, pp. 33–35. DOI: 10.21883/PJTF.2022.07.52290.19137,(In Russian)
  13. Gerasimov A. N. Generalization of linear deformation laws and their application to internal friction problems, Applied Mathematics and Mechanics, 1948, vol. 12, pp. 529–539.
  14. Caputo M. Linear models of dissipation whose Q is almost frequency independent – II, Geophysical Journal International, 1967, vol. 13, no. 5, pp. 529–539. DOI: 10.1111/j.1365-246X.1967.tb02303.x.
  15. Uchaikin V.V. Fractional Derivatives for Physicists and Engineers. Vol. I. Background and Theory. Berlin, Springer, 2013, 373 pp. DOI: 10.1007/978-3-642-33911-0.
  16. Westerlund S. Dead matter has memory!, Physica Scripta, 1991, vol. 43, no. 2, pp. 174–179. DOI: 10.1088/0031-8949/43/2/011.
  17. Patnaik S., Hollkamp J.P., Semperlotti F. Applications of variable-order fractional operators: a review, Proceedings of the Royal Society A, 2020, vol. 476, no. 2234, pp. 20190498. DOI: 10.1098/rspa.2019.0498.
  18. Lin R., Liu F., Anh V., Turner I. W. Stability and convergence of a new explicit finitedifference approximation for the variable-order nonlinear fractional diffusion equation, Applied Mathematics and Computation, 2009, vol. 212, no. 2, pp. 435–445. DOI:10.1016/j.amc.2009.02.047.
  19. Fang Z. W., Sun H. W., Wang H. A fast method for variable-order Caputo fractional derivative with applications to time-fractional diffusion equations, Computers & Mathematics with Applications, 2020, vol. 80, no. 5, pp. 1443–1458. DOI: 10.1016/j.camwa.2020.07.009.
  20. Sahoo S., Saha Ray S., Das S., Bera R. K. The formation of dynamic variable-order fractional differential equation, International Journal of Modern Physics C, 2016, vol. 27, no. 07, pp. 1650074. DOI: 10.1142/S0129183116500741.
  21. Tverdyi D. A., Parovik R. I. Investigation of Finite-Difference Schemes for the Numerical Solution of a Fractional Nonlinear Equation, Fractal and Fractional, 2022, vol. 6, no. 1:23, pp. 1–27. DOI: 10.3390/fractalfract6010023.
  22. Tverdyi D. A., Parovik R. I., Makarov E. O., Firstov P.P. Research of the process of radon accumulation in the accumulating chamber taking into account the nonlinearity of its entrance, E3S Web Conference, 2020, vol. 196, no. 02027, pp. 1–6. DOI: 10.1051/e3sconf/2020196020278.
  23. Tverdyi D. A., Makarov E. O., Parovik R. I. Hereditary Mathematical Model of the Dynamics of Radon Accumulation in the Accumulation Chamber, Mathematics, 2023, vol. 11, no. 4:850, pp. 1–20. DOI: 10.3390/math11040850.
  24. Tverdyi D. A., Makarov E. O., Parovik R. I. Research of Stress-Strain State of Geo-Environment by Emanation Methods on the Example of alpha(t)-Model of Radon Transport, Bulletin KRASEC. Physical and Mathematical Sciences, 2023, vol. 44, no. 3, pp. 86–104. DOI: 10.26117/2079-6641-2023-44-3-86-104.
  25. Rudakov V.P. Emanacionnyj monitoring geosred i processov [Emanational monitoring of geoenvironments and processes]. Moscow: Science World, 2009, 175 pp., (In Russian)
  26. Cox D. R. Hinkley D. V. Theoretical Statistics, 1st edition. London, Chapman & Hall/CRC, 1979, 528 pp., isbn: 9780412161605.
  27. Reviznikov D. L., Morozov A. Y. Algorithms for the numerical solution of fractional differential equations with interval parameters, Siberian journal of industrial mathematics, 2023, vol. 26, no. 4, pp. 93–108. DOI: 10.33048/SIBJIM.2023.26.407.
  28. Hadamard J. S. Sur les probl`emes aux derivees partielles et leur significa tion physique, Princeton University Bulletin, 1902, vol. 13, no. 4, pp. 49–52.
  29. Morozov V. A. Methods for Solving Incorrectly Posed Problems, New York: Springer, 1984, 257 pp., DOI: 10.1007/978-1-4612-5280-1.
  30. Mueller J. L., Siltanen S. Linear and Nonlinear Inverse Problems with Practical Applications. Philadelphia, USA, Society for Industrial and Applied Mathematics, 2012, 351 pp., isbn: 978-1-61197-233-7. DOI: 10.1137/1.9781611972344.
  31. Tarantola A. Inverse problem theory : methods for data fitting and model parameter estimation, Amsterdam and New York: Elsevier Science Pub. Co., 1987, 613 pp.
  32. Tahmasebi P., Javadpour F., Sahimi M. Stochastic shale permeability matching: Threedimensional characterization and modeling, International Journal of Coal Geology, 2016, pp. 231–242, vol. 165, no. 1. DOI: 10.1016/j.coal.2016.08.024.
  33. Lailly P. The seismic inverse problem as a sequence of before stack migrations, Conference on Inverse Scattering, Theory and application, 1983, pp. 206–220.
  34. Mohamad-Djafari A. Inverse Problems in Vision and 3D Tomography. New-York, ISTEWILEY, 2010, 480 pp., isbn: 9781848211728. DOI: 10.1002/9781118603864.
  35. Hayotov A. R., Jeon S., Shadimetov K. M. Application of optimal quadrature formulas for reconstruction of CT images, Journal of Computational and Applied Mathematics, 2021, vol. 388, pp. 113313. DOI: 10.1016/j.cam.2020.113313.
  36. Gubbins D. Book reviews. Inverse Problem Theory. Methods for Data Fitting and Model Parameter Estimation Albert Tarantola. Elsevier, Amsterdam and New York, 1987, Geophysical Journal International, 1988, vol. 94, no. 1, pp. 167–168. DOI: 10.1111/j.1365-246X.1988.tb03436.x.
  37. Tverdyi D. A., Parovik R. I. Hybrid GPU–CPU Efficient Implementation of a Parallel Numerical Algorithm for Solving the Cauchy Problem for a Nonlinear Differential Riccati Equation of Fractional Variable Order, Mathematics, 2023, vol. 11, no. 15:3358, pp. 1–21. DOI: 10.3390/math11153358.
  38. Tihonov A. N., Samarskij A. A. Uravneniya matematicheskoj fiziki [Mathematical physics equations]. Moskva: Nauka, 1977, 736 pp. (In Russian)
  39. Dennis J. E., Robert Jr., Schnabel B. Numerical methods for unconstrained optimization and nonlinear equations. Philadelphia, SIAM, 1983, 378 pp.
  40. Ivashchenko D. S. Chislennye metody resheniya pryamyh i obratnyh zadach dlya uravneniya diffuzii drobnogo poryadka po vremeni [Numerical methods of solution of direct and inverse problems for the fractional order time-dependent diffusion equation]. Diss. . . . PhD (Phys.-Math.). Tomsk. 2008. 187 p. (In Russian)
  41. Tihonov A. N. O reshenii nekorrektno postavlenny‘x zadach i metode regulyarizacii [On the solution of ill-posed problems and the method of regularization], Dokl. Akad. Nauk SSSR [Report AS USSR], 1963, vol. 151, no. 3, pp. 501–504 (In Russian)
  42. Kabanihin S. I., Iskakov K. T. Optimizacionnye metody resheniya koefficientnyh obratnyh zadach [Optimization methods for solving coefficient inverse problems]. Novosibirsk: Novosibirskij gosudarstvennyj universitet, 2001, 315 pp., isbn: 5-94356-022-X (In Russian)
  43. Kalitkin N. N. Chislennye metody. 2-e izd. [Numerical methods. 2nd ed.]. Saint Petersburg: BVH, 2011, 592 pp.,  (In Russian)
  44. Arridge S. R., Schweiger, M. A General Framework for Iterative Reconstruction Algorithms in Optical Tomography, Using a Finite Element Method, Computational Radiology and Imaging: Therapy and Diagnosticsn, 1999, vol. 110, pp. 40–70. DOI: 110.1007/978-1-4612-1550-9_4.
  45. Levenberg K. A method for the solution of certain non-linear problems in least squares, Quarterly of applied mathematics, 1944, vol. 2, no. 2, pp. 164–168. DOI: 10.1090/qam/10666.
  46. Marquardt D. W. An algorithm for least-squares estimation of nonlinear parameters, Journal of the society for Industrial and Applied Mathematics, 1963, vol. 11, no. 2, pp. 431–441. DOI: 10.1137/0111030.
  47. More J. J. The Levenberg-Marquardt algorithm: Implementation and theory, In: Watson, G.A. (eds) Numerical Analysis. Lecture Notes in Mathematics, 1978, vol. 630, pp. 105–116. DOI: 10.1007/BFb0067700.
  48. Borzunov S. V., Kurgalin S. D., Flegel A. V. Praktikum po parallel’nomu programmirovaniyu: uchebnoe posobie [Workshop on Parallel Programming: A Study Guide]. Saint Petersburg: BVH, 2017, 236 pp.,  (In Russian)
  49. Sanders J., Kandrot E. CUDA by Example: An Introduction to General-Purpose GPU Programming. London, Addison-Wesley Professional, 2010, 311 pp.

Information about the authors

Tverdyi Dmitrii Alexsandrovich – PhD (Phys. & Math.), Researcher at the Electromagnetic Radiation Laboratory, Institute of Cosmophysical Research and Radio Wave Propagation, FEB RAS, Paratunka village, Russia, ORCID 0000-0001-6983-5258.


Parovik Roman Ivanovich – D. Sci. (Phys. & Math.), Associate Professor, Leading researcher laboratory of modeling physical processes Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, Paratunka village, Russia, ORCID 0000-0002-1576-1860.