Evolutionary Optimization of Runge-Kutta Coefficients for Specific Differential Equations

dc.contributor.author Kulas, Benjamin
dc.date.accessioned 2019-08-09T16:29:01Z
dc.date.available 2019-08-09T16:29:01Z
dc.date.issued 2019-05-02
dc.description.abstract Methods of numerical integration in the Runge-Kutta family are parametrized by several arrays of coefficients used in the integration process. These coefficients can take any value and still form a valid integration method, so long as they are properly normalized. This makes them well-suited to optimization by evolutionary algorithms. A program named deltaRK is introduced which uses an evolutionary algorithm to produce an integrator specialized for simulating a single differential equation. deltaRK currently only supports differential equations with an associated conserved quantity such as energy or angular momentum. Integrators produced by deltaRK are more accurate on the trained system than standard Runge-Kutta integrators with no loss in speed. en_US
dc.identifier.uri http://jewlscholar.mtsu.edu/xmlui/handle/mtsu/6033
dc.language.iso en_US en_US
dc.publisher University Honors College, Middle Tennessee State University en_US
dc.subject numerical integration en_US
dc.subject Runge-Kutta methods en_US
dc.subject machine learning en_US
dc.subject genetic algorithms en_US
dc.subject orbital simulation en_US
dc.subject evolutionary algorithms en_US
dc.title Evolutionary Optimization of Runge-Kutta Coefficients for Specific Differential Equations en_US
dc.type Thesis en_US
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