ASTRO 406 - Computational Astrophysics
Fall 2021, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Astronomy (ASTRO)
Department: LSA Astronomy
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Requirements & Distribution:
Waitlist Capacity:
Advisory Prerequisites:
Prior or concurrent enrollment in MATH 216, 256 or 286, prior or concurrent enrollment in PHYSICS 235, 240 or 260, and some knowledge of programming.
Other Course Info:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May not be repeated for credit.
Primary Instructor:


Computational Astrophysics develops a practical working knowledge of the numerical methods most widely used in current research. For each method, we briefly discuss the underlying theory and then put it into practice by coding and using numerical routines for specific research applications. All coding is done in python, using interactive IPython notebooks. We first cover the most common scientific methods, such as interpolation, integration, differentiation, and statistical description of data. We then study in-depth data modeling and parameter fitting, using both maximum likelihood and Bayesian methods. For a particularly common task of linear regression, we compare these direct methods with Markov Chain Monte Carlo sampling. We apply various machine learning methods to analyze and classify complex datasets. We also consider illustrative examples of solving systems of ordinary and partial differential equations. The course concludes with student projects, where these methods are used for real-life research applications.


ASTRO 406 - Computational Astrophysics
Schedule Listing
001 (LEC)
 In Person
MW 11:30AM - 1:00PM

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