Applied Computational Physics and Machine Learning (FYSN33/NTF014F)

Welcome to the canvas information page for the course

Applied Computational Physics and Machine Learning, 7.5 credits

given by the Department of Physics.

Note: this page contains general information about the course. If you are a student on the course you have to log in to the canvas portal.

Content

In contemporary physics computers play an integral part. In particular, numerical methods provide means for solving problems that are intractable by other methods (and often more interesting!).

In this course you will learn a number of different numerical techniques for solving physics problems in mechanics, statistical physics, electromagnetism, stochastic processes and quantum mechanics, which are used in current research.

The course is organized into a three modules. The examination is in the form of three project reports/presentations (one for each module) and a written examination.

Some basic knowledge of programming, for instance in Java, C++ or Matlab, is helpful. 

General information

• Course codes: FYSN33, NTF014F (PhD)
• Semester: ​autumn
• Study period: ​1
• Level: ​master
• Language: English
• Forms of teaching: Lectures, Programming Projects
• Assessment: Project Reports, Written Exam
• Grading scale: U-G-VG (U-G for the PhD course)
• Previous course code: FYTN03

Syllabus

• Syllabus for Applied Computational Physics and Machine Learning in English
• Syllabus for Applied Computational Physics and Machine Learning in Swedish
• Syllabus for Applied Computational Physics and Machine Learning, PhD course (to be updated)

Course literature

• The main book complementing the lecture notes is: Basic Concepts in Computational Physics Links to an external site., 2nd ed. by B.A. Stickler and E. Schachinger (Springer). This book is available as e-book through Lund university libraries (click link above).
• Alternative course book: Computational Physics, 2nd ed. Links to an external site. by N.J. Giordano and H. Nakanishi, (Pearson Education). Note that there are a few misprints in this book, click here to see corrections Links to an external site..
• Further reading: The Numerical Recipes book is a useful book for a more detailed discussion of the numerical methods: Numerical Recipes : The Art of Scientific Computing, 3rd ed. by W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery, (Cambridge University Press). NOTE: The earlier editions of the Numerical Recipes books are available for free online.

Course coordinator

Victor Olariu, email: victor.olariu@cec.lu.se, ​​phone: 046-2223496

Schedule

The schedule for the next instance of the course can be found here on TimeEdit. Links to an external site.

Application

Science faculty

Read more about the course and how to apply via below links. Please note: to see if the course is open for application, always check the Swedish web page.

• Read about the course FYSN33 on Lund University's central website (in English).
• Read about the course FYSN33 on Lund University's central website (in Swedish).

Doctoral studies

Sign up for the course by contacting course responsible and forskarutbildning@fysik.lu.se.

Remember that you need to have the course listed in your individual study plan, ISP, or approved by your supervisor to have it counted towards your exam.

Read more

• All courses offered by the Department of Physics (on fysik.lu.se)
• Degree project page on Canvas
• Course evaluations
• Canvas page for physics students

Updated December 16, 2024.