2020/06/01 by Alexander N. Pchelintsev · 2 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Artificial intelligence #Chaos control and synchronization #Chaotic #Chaotic systems #Computer science #Dynamical systems theory #Lyapunov exponent #Mathematical analysis #Mathematics #Nonlinear Dynamics and Pattern Formation #Numerical analysis #Physics #Process (computing) #advanced mathematical theories #cs.NA #math.DS #math.NA #msc:34 #msc:65 #physics.comp-ph
paper · pdf · doi:10.5890/jand.2020.06.004
published as https://www.lhscientificpublishing.com/Journals/JAND-Download.aspx?volume=2020&issue=2 · Journal of Applied Nonlinear Dynamics, 9:2 (2020), 207-221
openalex publication_date 2020/06/01 · arxiv created 2020/11/21 · arxiv updated 2020/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In various fields of natural science, the chaotic systems of differential equations are considered more than 50 years. The correct prediction of the behaviour of solutions of dynamical model equations is important in understanding of evolution process and reduce uncertainty. However, often used numerical methods are unable to do it on large time segments. In this article, the author considers the modern numerical method and algorithm for constructing solutions of chaotic systems on the example of tumor growth model. Also a modification of Benettin's algorithm presents for calculation of Lyapunov exponents.