An easy (but not so short) introduction to applied numerical computing
In this series, we take a look at the fundamental concepts of numerical computing to solve partial differential equations.
Educational Materials
In order to follow the videos, you need the educational materials and content, which are provided as a set of Jupyter Notebooks. You can find the materials in this GitHub repository
Videos
- An easy (but not so short) introduction to applied numerical computing
- Introduction to Jupyter notebooks
- A quick overview of Python programming language
- A short tutorial on NumPy, multi-dimensional arrays and matrices library
- SciPy in a nutshell, the library of scientific algorithms for Python
- Essential Matplotlib, data plotting library for Python
- A quick look at SymPy for symbolic computation in Python
- Use SymPy for solving ordinary differential equations in Python
- High-performance computing and parallel programming in Python
- A short git tutorial; introducing version control systems
- Why numerical methods matter? Why we need them for computer simulations?
- Finite difference method for (nonlinear) ordinary differential equations (ODEs)
. - Finite difference method for solving partial differential equations (PDEs)
- Stability condition and higher-order methods for numerical solution of PDEs
- Finite difference solution of the diffusion equation
- Iterative solution of differential equations using finite difference method
- All you need to know from finite element theory | Part 1 | approximation using basis functions
- All you need to know from finite element theory | Part 2 | variational and weak formulation of PDEs
- All you need to know from finite element theory | Part 3 | time-dependent and nonlinear problems
