In this course we will use mainly the Scientific Computing packages Python, MatLab and Octave.
See below a few links to useful resources on these packages. You are not supposed to read/use all these resources at once, they are listed here as a reference for you throughout the semester.
Python
- A Gentle Introduction to Numerical Simulations with Python
- Scientific Computing and Simulation Slides
- Python tutorial for a first course in Numerical Differential Equations
- SymPy tutorial for a first course in Numerical Differential Equations
- Using Python to solve PDEs
- Think Python: How to think like a computer scientist
- A Crash Course in Python for Scientists
- Python Scientific Lecture Notes
- Python for Computational Science and Engineering, by H. Fangohr (U. of Southampton).
- A Primer on Scientific Programming with Python, by H.P. Langtangen (U. of Oslo).
- SciPy Lecture Notes.
- Run your python code online with Skulp, an entirely in-browser implementation of Python.
- SymPy, a Python library for symbolic mathematics.
- Use SymPy online.
- Use NumPy online. NumPy is the fundamental package for scientific computing with Python and, in particular, the foundation of the SciPy package.
MatLab
- A Gentle Introduction to Numerical Simulations with MATLAB
- Scientific Computing in Matlab
- A practical introduction to MATLAB
- MATLAB tutorial on numerical ODEs
- ODE Software for MATLAB
- MATLAB tutorial on numerical PDEs
- A nice introduction to MatLab, by C. Moler (Mathworks).
- MatLab tutorial at Tutorials Point.
- A set of very nice slides introducing Scientific Computing with MatLab, by G.W. Recktenwald (PSU): 2, 3, 4.
- An introduction to MatLab, by D. Griffiths, U. of Dundee.