Research Article
BibTex RIS Cite
Year 2023, Volume: 6 Issue: 2, 42 - 48, 07.08.2023
https://doi.org/10.33187/jmsm.1234247

Abstract

References

  • [1] H. Murakawa, A linear scheme to approximate nonlinear cross-diffusion systems, Esaim Math. Model. Numer. Anal., 45(6) (2011), 1141-1161.
  • [2] N. Shigesada, K. Kawasaki, E. Teramoto, Spatial segregation of interacting species, J. Theor. Biol., 79 (1) (1979), 83-99.
  • [3] H. Murakawa, A linear finite volume method for nonlinear cross-diffusion systems, Numer. Math., 136 (1) (2017), 1-26.
  • [4] H. Murakawa, A solution of nonlinear diffusion problems by semilinear reaction-diffusion systems, Kybernetika, 45 (4) (2009), 580-590.
  • [5] W. J. Barrett, F. J. Blowey, Finite element approximation of a nonlinear cross-diffusion population model, Numer. Math., 98 (2) (2004), 195-221.
  • [6] L. Chen, A. J¨ungel, Analysis of a parabolic cross-diffusion population model without self-diffusion, J. Differ. Equ., 224 (1) (2006), 39-59.
  • [7] B. Andreianov, M. Bendahmane, R. Ruiz-Baier, Analysis of a finite volume method for a cross-diffusion model in population dynamics, Math. Models Methods Appl. Sci. 21 (02) (2011), 307-344.
  • [8] B. Riviere, Galerkin Methods for Solving Elliptic and Parabolic Equations: Theory and Implementation, SIAM, 2008.
  • [9] H. Murakawa, Cross-diffusion systems: RDS approximation and numerical analysis, Publications of the Research Institute for Mathematical Sciences, 1924 (2014), 21–29.
  • [10] K. Kunisch, S. Volkwein, Galerkin proper orthogonal decomposition methods for parabolic problems, Numer. Math., 90 (1) (2001), 117-148.
  • [11] S. Volkwein, Proper orthogonal decomposition: Theory and reducedorder modelling, Lecture Notes, University of Konstanz, 4 (4) (2013), 1-29.
  • [12] B. Karasözen, G. Mülayim, M. Uzunca, S. Yılıdz, Reduced order modelling of nonlinear cross-diffusion systems, Appl. Math. Comput., 401 (2021), 126058.
  • [13] M. Barrault, Y. Maday, N. C. Nguyen, A. T. Patera, An empirical interpolation method: application to effcient reduced-basis discretization of partial differential equations, Comptes Rendus Math., 339 (9) (2004), 667-672.
  • [14] S. Chaturantabut, D. C. Sorensen, Nonlinear model reduction via discrete empirical interpolation, SIAM J. Scı. Comput., 32 (5) (2010), 2737-2764.
  • [15] S. Chaturantabut, D. C. Sorensen, A state space error estimate for POD-DEIM nonlinear model reduction, SIAM J. Numer. Anal., 50 (1) (2012), 46-63.
  • [16] B. Karasözen, T. Küçükseyhan, M. Uzunca, Structure preserving integration and model order reduction of skew-gradient reaction-diffusion systems, Ann. Oper. Res., 258 (1) (2017), 79-106.
  • [17] N. Halko, P. G. Martinsson, J. A. Tropp, Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM Review, 53 (2) (2011), 217-288.
  • [18] M. W. Mahoney, Randomized algorithms for matrices and data, Found. Trends Mach. Learn., 3 (2) (2011), 123-224.
  • [19] G. Gambino, M. Lombardo, M. Sammartino, Pattern formation driven by cross-diffusion in a 2D domain, Nonlinear Anal. Real World Appl., 14 (3) (2013), 1755-1779.

Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems

Year 2023, Volume: 6 Issue: 2, 42 - 48, 07.08.2023
https://doi.org/10.33187/jmsm.1234247

Abstract

Shigesada-Kawasaki-Teramoto (SKT) is the most known equation in population ecology for nonlinear cross-diffusion systems. The full order model (FOM) of the SKT system is constructed using symmetric interior penalty discontinuous Galerkin method (SIPG) in space and the semi-implicit Euler method in time. The reduced order models (ROMs) are solved using proper orthogonal decomposition (POD) Galerkin projection. Discrete empirical interpolation method (DEIM) is used to solve the nonlinearities of the SKT system. Numerical simulations show the accuracy and efficiency of the POD and POD-DEIM reduced solutions for the SKT system.

References

  • [1] H. Murakawa, A linear scheme to approximate nonlinear cross-diffusion systems, Esaim Math. Model. Numer. Anal., 45(6) (2011), 1141-1161.
  • [2] N. Shigesada, K. Kawasaki, E. Teramoto, Spatial segregation of interacting species, J. Theor. Biol., 79 (1) (1979), 83-99.
  • [3] H. Murakawa, A linear finite volume method for nonlinear cross-diffusion systems, Numer. Math., 136 (1) (2017), 1-26.
  • [4] H. Murakawa, A solution of nonlinear diffusion problems by semilinear reaction-diffusion systems, Kybernetika, 45 (4) (2009), 580-590.
  • [5] W. J. Barrett, F. J. Blowey, Finite element approximation of a nonlinear cross-diffusion population model, Numer. Math., 98 (2) (2004), 195-221.
  • [6] L. Chen, A. J¨ungel, Analysis of a parabolic cross-diffusion population model without self-diffusion, J. Differ. Equ., 224 (1) (2006), 39-59.
  • [7] B. Andreianov, M. Bendahmane, R. Ruiz-Baier, Analysis of a finite volume method for a cross-diffusion model in population dynamics, Math. Models Methods Appl. Sci. 21 (02) (2011), 307-344.
  • [8] B. Riviere, Galerkin Methods for Solving Elliptic and Parabolic Equations: Theory and Implementation, SIAM, 2008.
  • [9] H. Murakawa, Cross-diffusion systems: RDS approximation and numerical analysis, Publications of the Research Institute for Mathematical Sciences, 1924 (2014), 21–29.
  • [10] K. Kunisch, S. Volkwein, Galerkin proper orthogonal decomposition methods for parabolic problems, Numer. Math., 90 (1) (2001), 117-148.
  • [11] S. Volkwein, Proper orthogonal decomposition: Theory and reducedorder modelling, Lecture Notes, University of Konstanz, 4 (4) (2013), 1-29.
  • [12] B. Karasözen, G. Mülayim, M. Uzunca, S. Yılıdz, Reduced order modelling of nonlinear cross-diffusion systems, Appl. Math. Comput., 401 (2021), 126058.
  • [13] M. Barrault, Y. Maday, N. C. Nguyen, A. T. Patera, An empirical interpolation method: application to effcient reduced-basis discretization of partial differential equations, Comptes Rendus Math., 339 (9) (2004), 667-672.
  • [14] S. Chaturantabut, D. C. Sorensen, Nonlinear model reduction via discrete empirical interpolation, SIAM J. Scı. Comput., 32 (5) (2010), 2737-2764.
  • [15] S. Chaturantabut, D. C. Sorensen, A state space error estimate for POD-DEIM nonlinear model reduction, SIAM J. Numer. Anal., 50 (1) (2012), 46-63.
  • [16] B. Karasözen, T. Küçükseyhan, M. Uzunca, Structure preserving integration and model order reduction of skew-gradient reaction-diffusion systems, Ann. Oper. Res., 258 (1) (2017), 79-106.
  • [17] N. Halko, P. G. Martinsson, J. A. Tropp, Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM Review, 53 (2) (2011), 217-288.
  • [18] M. W. Mahoney, Randomized algorithms for matrices and data, Found. Trends Mach. Learn., 3 (2) (2011), 123-224.
  • [19] G. Gambino, M. Lombardo, M. Sammartino, Pattern formation driven by cross-diffusion in a 2D domain, Nonlinear Anal. Real World Appl., 14 (3) (2013), 1755-1779.
There are 19 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Gülden Mülayim 0000-0001-8952-7658

Publication Date August 7, 2023
Submission Date January 14, 2023
Acceptance Date March 6, 2023
Published in Issue Year 2023 Volume: 6 Issue: 2

Cite

APA Mülayim, G. (2023). Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems. Journal of Mathematical Sciences and Modelling, 6(2), 42-48. https://doi.org/10.33187/jmsm.1234247
AMA Mülayim G. Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems. Journal of Mathematical Sciences and Modelling. August 2023;6(2):42-48. doi:10.33187/jmsm.1234247
Chicago Mülayim, Gülden. “Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems”. Journal of Mathematical Sciences and Modelling 6, no. 2 (August 2023): 42-48. https://doi.org/10.33187/jmsm.1234247.
EndNote Mülayim G (August 1, 2023) Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems. Journal of Mathematical Sciences and Modelling 6 2 42–48.
IEEE G. Mülayim, “Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems”, Journal of Mathematical Sciences and Modelling, vol. 6, no. 2, pp. 42–48, 2023, doi: 10.33187/jmsm.1234247.
ISNAD Mülayim, Gülden. “Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems”. Journal of Mathematical Sciences and Modelling 6/2 (August 2023), 42-48. https://doi.org/10.33187/jmsm.1234247.
JAMA Mülayim G. Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems. Journal of Mathematical Sciences and Modelling. 2023;6:42–48.
MLA Mülayim, Gülden. “Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems”. Journal of Mathematical Sciences and Modelling, vol. 6, no. 2, 2023, pp. 42-48, doi:10.33187/jmsm.1234247.
Vancouver Mülayim G. Reduced Order Modelling of Shigesada-Kawasaki-Teramoto Cross-Diffusion Systems. Journal of Mathematical Sciences and Modelling. 2023;6(2):42-8.

29237    Journal of Mathematical Sciences and Modelling 29238

                   29233

Creative Commons License The published articles in JMSM are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.