262012 VO Introduction to Computational Astrophysics (2023S)
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Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: English
Examination dates
- Tuesday 27.06.2023 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Friday 06.10.2023 13:15 - 14:45 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 27.11.2023 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Monday 29.01.2024 13:15 - 14:45 Seminarraum 2 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 03.09.2024
Lecturers
Classes (iCal) - next class is marked with N
Location: Seminar room 1 (Institute of Astrophysics)
- Tuesday 07.03. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 14.03. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 21.03. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 28.03. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 18.04. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 25.04. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 02.05. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 09.05. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 16.05. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 23.05. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 06.06. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 13.06. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
- Tuesday 20.06. 11:30 - 13:00 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Information
Aims, contents and method of the course
Assessment and permitted materials
Written exam -- dates: 27.06.23 / 06.10.23 / 27.11.23 / 29.01.24
Test documents: Lectures of VO (on moodle)
Positive grades: more than 50% of the questions must be answered correctly
No resources (slides) are allowed during the examination
Test documents: Lectures of VO (on moodle)
Positive grades: more than 50% of the questions must be answered correctly
No resources (slides) are allowed during the examination
Minimum requirements and assessment criteria
More than 50% of the questions must be answered correctly;
4 Questions per topic and each question counts 3 points;
Grading: 48-43 points Sehr gut (1)
42-37 points Gut (2)
36-31 points Befriedigend (3)
30-25 points Genügend (4)
<25 points Nicht Genügend (5)
4 Questions per topic and each question counts 3 points;
Grading: 48-43 points Sehr gut (1)
42-37 points Gut (2)
36-31 points Befriedigend (3)
30-25 points Genügend (4)
<25 points Nicht Genügend (5)
Examination topics
Slides of lectures (on moodle)
Part I : Computational Fluid Dynamics for Astrophysics I - III
Part II: Inference with Probabilistic Programming Languages (PPLs) I - III
Part III: N-body dynamics I (slides 31-44) & N-body dynamics II - III
Part IV: Astronomical Data and Data Processing I - III
Part I : Computational Fluid Dynamics for Astrophysics I - III
Part II: Inference with Probabilistic Programming Languages (PPLs) I - III
Part III: N-body dynamics I (slides 31-44) & N-body dynamics II - III
Part IV: Astronomical Data and Data Processing I - III
Reading list
will be provided during the lectures;
(provided on moodle)
(provided on moodle)
Association in the course directory
ICA
Last modified: Th 08.08.2024 00:15
-- Computational fluid mechanics for compressible flows (topics include von Neumann stability, upwind schemes, Godunov schemes, higher order methods, and source terms)
-- Nbody Dynamics (different methods to compute the N-body interaction; classical and symplectic methods and applications)
-- Astronomical Data and Data Processing
-- Inference with Probabilistic Programming Languages (PPLs): Generative models, Bayesian networks, Gradient based inference (Hamiltonian Monte Carlo & Variational Inference); Astronomical applications