TN2513 Computational Science 2019/2020

Overall course learning objectives

A listing of our overall somewhat philosophical learning objecgtives:

• The student can design and implement basic algorithms in Python.
• The student can implement simple analytical and numerical mathematical operations in a numerical programming environment.
• The student can analyze the effect of data resolution, numerical precision, convergence, memory load, and computational speed on a solution of a numerical problem.
• The student can implement numerical methods in a given physical problem setting.

Detailed learning objectives

A detailed list of the things you should be able to do in order to pass the course:

Detailed learning objectives

Zip file

In this zip file, you can find all the jupyter notebooks and files needed to work on the lecture material on your own computer, including answer checking:

Lecture notebooks zip file

Lecture HTML Previews

01 Introduction to programming in Python 1

• 1c - Python program flow control
• 1a - Jupyter Notebooks and basic Python
• 1b - Functions in Python

02 Introduction to programming in Python 2

• 2b Data visualization and curvefitting
• 2e Good coding practices
• 2a Scientific Computing with Numpy
• 2d Additional Programming Concepts - Optional
• 2c Implementing algorithms

03 Numerical Differentiation

• 3 Differentiation

04 Numerical Integration

• 4 Integration

05 Root finding

• 5 Root finding

06 Linear algebra

• 6 Linear algebra

07 Fourier transforms 1

• 7 Fourier transforms 1

08 Fourier transforms 2

• 8 Fourier transforms 2

09 Random numbers

• 09 Random numbers

10 Monte Carlo simulations

• 10 Monte Carlo simulations

11 Ordinary Differential Equations 1

• 11 Ordinary Differential Equations Part 1

12 Ordinary Differential Equations 2

• 12 Ordinary Differential Equations Part 2

13 Partial Differential Equations 1

• 13 Partial Differential Equations 1 - Boundary Value Problems

14 Partial Differential Equations 2

• 14 Initial Value Problems with PDEs