Numerical Methods in Physics

Course Information

All details regarding the location, schedule, and ECTS credits are available on KSL.

Lecture Description

By the end of the course, students will be able to:

• Distinguish between random, systematic, and gross errors (outliers).
• Understand the general law of error propagation and apply it to various practical cases.
• Understand the principle of the method of least squares and explain its widespread use in practice.
• Be familiar with the concepts of indirect and conditional least-squares adjustments, distinguish between them, and combine them as appropriate.
• Are able to use the fundamental features of the Python programming language independently.
• Are able to implement and solve both linear and non-linear adjustment problems using Python or another programming language.
• Are familiar with common data filtering methods and can implement them independently in Python or another programming language.
• Are able to evaluate the results of a least-squares adjustment using appropriate statistical methods.
• Place Fourier analysis in the context of least-squares adjustment and understand its significance for data filtering.
• Understand the transition from sequential adjustment methods to the Kalman optimal filter.