Jupyter Notebooks for numerical approximation of ordinary differential equations

<b>Jupyter Notebooks to illustrate basic aspects of numerical approximation to Ordinary Differential Equations</b>This archive contains some Python scripts based on NumPy and SciPy, notably. These scripts illustrate how differential equations can be solved using basic numerical methods.These scripts are encapsulated in Jupyter Notebooks.They illustrate :A) Basic methodological aspects (Euler, Runge-Kutta approximation, convergence order, etc).<i>Euler.ipynb ; </i><i>EulerImplicito.ipynb </i><i>; Newton.ipynb </i><i>; orderMethods.ipynb </i><i>; roundoff.ipynb </i><i>; Runge Kutta.ipynb </i><i>; Verlet.ipynb</i>B) Applications to well-known cases<i>dogjogger.ipynb ; </i><i>Lorentz.ipynb </i><i>; LotkaVolterra.ipynb ; </i><i>OscilladorHarmonico.ipynb </i><i>; vanderpol.ipynb</i>C) More advanced issues (time-parallel time-integration).<i>Parareal.ipynb</i><br>