Desarrollo de ecuaciones diferenciales de segundo orden para el aprendizaje de física computacional en python 3.10.0.

Abstract

During the health emergency, the teachers, and students of the Physics course at the Escuela Superior Politécnica de Chimborazo have not had access to the laboratories and have implemented the use of virtual laboratories. For this reason, this integrative work proposal was presented in which the different practices that can be carried out with the Python program will be put into operation. In this work, the importance of developing second order differential equations for learning computational physics in Python 3.10.0 was determined; to carry it out, we worked on a type of literature review research as it can be applied to any research topic and determine the relevance and importance of this and ensure the originality of a research. It is also experimental in nature, as exercises have been carried out in Python software. The results show that the use of the fourth order Runge-Kutta method is ideal for solving second-order and N-order differential equations, since it is only necessary to know the methods of equation reduction and thus be able to apply this algorithm, it also provides a small margin of error with respect to the real solution of the problem, and it is easily programmable in Python to carry out the necessary iterations. It is concluded that, when developing the second order differential equations exercises, Python is a very powerful computational physics tool through its "numpy" and "matplotlib" libraries, since "mumpy" performs first order ODEs, which allowed performing the ODE calculations by applying the RungeKutta method, and "matplotlib" allows the results obtained to be plotted in a suitable way. Finally, it is recommended to use the variables described in this work and at the same time to elaborate a methodological guide of the same.