Computational Astrophysics

Course content

The course gives an introduction to numerical methods for contemporary computational astrophysics. It covers theory and practice for numerical methods including fluid and particle dynamics, gravitational collapse, and radiative energy transfer. It gives an overview of computational models for microphysical processes, such as cooling, heating, dust dynamics, and astrochemistry. The course exercises introduce and illustrate the methods applying them to concrete examples from astrophysics, and give a “hands-on” feeling for how and in what context they are used. During the course exercises, the students will build a highly modular yet simple core program based on Jupyter notebooks written in python, which includes most of the methods covered in the lectures. The course also touches on technical aspects, such as high performance computing and efficient code development.

Education

MSc Programme in Physics

MSc Programme in Physics with a minor subject

Learning outcome

Knowledge:
The student will come to know the fundamental equations that govern astrophysical dynamics, including fluids, magnetic fields, radiative energy transfer and coupled gas-particle interaction, and how to solve them with modern numerical methods. In addition, the student will achieve knowledge of the basic computational techniques used in modern astrophysics, including the principles of adaptive mesh refinement techniques and the difference between mesh and particle methods.

 

Skills:

  • Modelling the dynamics of the interstellar medium, including fluids, magnetic fields, and heating and cooling.
  • Modelling gravitational collapse
  • Solving the radiation transfer equation
  • Using radiative transfer in connection with analysis and modelling of observations
  • Modelling particle dynamics and gas-particle interaction

 

Competences:
The course gives basic competences in numerical modelling, and will establish a foundation for an MSc project based on numerical modelling.

Lectures and exercises

See Absalon for final course material. The following is an example of expected course literature.

 

P. Bodenheimer, G. P. Laughlin, M. Rozyczka, T. Plewa, H. W. Yorke: “Numerical Methods in Astrophysics”.  Complemented with lecture notes.

The student is expected to have a basic understanding of astrophysics such as that covered by the BSc courses “Stars and Planets” and “Extragalactic Astrophysics”. It is required to have some programming experience in Python, such as that acquired in “Introduction to Computing for Physicists”. It is recommended but not required that the student has followed an MSc course on theoretical astrophysics or has some knowledge about fluid dynamics.

Academic qualifications equivalent to a BSc degree is recommended.

This course is identical to the discontinued course NFYK14018U Computational Astrophysics: Star and Planet Formation

ECTS
7,5 ECTS
Type of assessment
Oral examination, 20 minutes (no preparation time)
Aid
No aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as ordinary exam

Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 142,5
  • Practical exercises
  • 28
  • Exam
  • 0,5
  • English
  • 206,0

Kursusinformation

Language
English
Course number
NFYK21004U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 2
Schedulegroup
A
Capacity
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Physics, Chemistry and Nanoscience
Contracting department
  • The Niels Bohr Institute
  • GLOBE Institute
Contracting faculty
  • Faculty of Science
Course Coordinators
  • Anders Johansen   (15-457268697677324e736c65727769724477797268326f7932686f)
  • Michiel Thomas A Lambrechts   (18-716d676c6d697032706571667669676c78774477797268326f7932686f)
  • Troels Haugbølle   (8-6a6377696471676e4270646b306d7730666d)
Saved on the 12-08-2026

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