There are 2 repositories under runge-kutta-methods topic.
A numerical integrator written in Elixir for the solution of sets of non-stiff ordinary differential equations (ODEs).
Electric field lines and equipotentials using Runge-Kutta methods, including adaptive ones
常微分方程式の数値解法の基礎.(教材)
3D animation of the Lorenz Attractor trajectory, implemented in Python using the 4th order Runge-Kutta method. [Personal project]
Python solver for the Brownian, Stochastic, or Noisy Differential Equations
Competitive Lotka–Volterra equations, solved using Runge-Kutta methods. Four dimensional system.
Set of modern Fortran numerical methods.
Solving some interesting problems using Python and C++
Differents algorithms on python or matlab about numerical analysis - UNI
C++ library of (Ordinary) Differential Equations solvers.
Numerical Method Codes in Python
A collection of various methods to find solution to Ordinary Differential Equations.
Works about Cucker-Smale model and its extensions. =Keywords: ODE, Runge-Kutta methods, SDE, Euler-Maruyama method, NumPy, Matplotlib
The motion of an asteroid in a two-dimensional space with a star and a planet • University project • 2014 - Laboratorio di Fisica Computazionale - BSc in Physics, II year
The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.
Contains sample implementations in python of the following numerical methods: Euler's Method, Midpoint Euler's Method, Runge Kuttta Method of Order 4, and Composite Simpson's Rule
Implementation of several popular solvers for solving ODEs in MATLAB.
Mathematical model based on the numerical analysis of ordinary differential equations for the capture of satellite positions and velocities
A collection of classical algorithms to solve ODEs and boundary value problems
A numerical method is an approximate computer method for solving a mathematical problem which often has no analytical solution.
Simple header-only C++ ODE solver library for Runge-Kutta 4th order method with events.
Projet en C++ | Réalisation d'un moteur de résolution d'EDO et illustration de la théorie du contrôle linéaire
CODE FOR ALL MODULES OF NUMERICAL METHODS
N-body space simulator that uses the Runge-Kutta 4 numerical integration method to solve two first order differential equations derived from the second order differential equation that governs the motion of an orbiting celestial. Also has preset demos for two-body and three-body circular orbits which use parametric equations. Uses the SFML (Simple and Fast Multimedia Library) library: https://www.sfml-dev.org
A modern Fortran library providing an object-oriented approach to solving ordinary differential equations.
The assignments of the course EE1103, Numerical Methods, offered by Prof. Anil Prabhakar, Department of Electrical Engineering at IIT Madras.
Implementations of various Algorithms used in Numerical Analysis, from root-finding up to gradient descent and numerically solving PDEs.
My codes for the courses of Computer Programming, and Numerical and Computational Physics at IITR
Python and MATLAB code to find the solution of First Order Differential Equations given a certain initial/boundary condition.
Numerical solutions of differential equations: Eulers, Euler-Cauchy, Runge-Kutta, midpoint method
The physical model of the projectile flight
A solution to the Lotka-Volterra Equations is approximated using Dormand-Prince-45 method with adaptive step size control.
Package for the generation of coefficients used in Spectral Deferred Correction and related methods (Runge-Kutta, ...)