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Reduced Basis Approximation for a Spatial Lotka-Volterra Model

Author

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  • Peter Rashkov

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, ul. Akademik Georgi Bonchev, Blok 8, 1113 Sofia, Bulgaria)

Abstract

We construct a reduced basis approximation for the solution to a system of nonlinear partial differential equations describing the temporal evolution of two populations following the Lotka-Volterra law. The first population’s carrying capacity contains a free parameter varying in a compact set. The reduced basis is constructed by two approaches: a proper orthogonal decomposition of a collection of solution snapshots and a greedy algorithm using an a posteriori error estimator.

Suggested Citation

  • Peter Rashkov, 2022. "Reduced Basis Approximation for a Spatial Lotka-Volterra Model," Mathematics, MDPI, vol. 10(12), pages 1-12, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:1983-:d:834304
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    Cited by:

    1. Jonggeon Lee & Euiyoung Kim & Jaehun Lee, 2022. "Data Reconstruction-Based Two-Step Non-Intrusive Reduced-Order Modeling Using Fourier Transform and Interpolations," Mathematics, MDPI, vol. 10(20), pages 1-16, October.

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