This website is no longer updated.

As of 1.10.2022, the Faculty of Physics has been merged into the TUM School of Natural Sciences with the website https://www.nat.tum.de/. For more information read Conversion of Websites.

de | en

Scientific Computing 1 (IN2005)

Course 0000001358 in WS 2023/4

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 5 - Chair of Scientific Computing (Prof. Bungartz)
Lecturers Chinmay Datar
Kislaya Ravi
Responsible/Coordination: Michael Georg Bader
Dates Wed, 10:00–12:00, MI HS2
and 1 singular or moved dates

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks The module introduces the steps of the scientific computing simulation pipeline on selected simulation scenarios, focusing especially on aspects of modelling and discretization: - classification of mathematical models (discrete/continuous, deterministic/stochastic, etc.); - discrete models (e.g. Markov chain models) - modeling with ordinary differential equations for the example of population growth; - numerical solution of systems of ordinary differential equations; - modeling with partial differential equations (PDE) for the example of fluid dynamics; - numerical discretization methods for partial differential equations (finite elements, time stepping, grids and adaptivity); - limitations and errors encountered in models and discretized models - adequacy and asymptotic behavior of models (stability, consistency, accuracy, and convergence of numerical methods) An outlook will be given on the impact that further steps of the simulation pipeline can have on the selection of modeling and discretization techniques, such as: - efficient sequential and parallel implementation (architectures, parallel programming, load distribution, domain decomposition, parallel numerical methods); - visualization or results - embedding of simulations in larger simulation environments (coupled models, workflows for parameter studies and uncertainty quantification) Examples are primarily selected from societally and economically relevant simulation scenarios, such as: - Discrete and continuous population models (incl. spreading of diseases, traffic simulation, use of population-type models in economy) - Simulation of hazards (e.g. shallow water models for tsunami simulation) - Computational fluid dynamics (towards weather and climate simulation)
Links Course documents
E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
WS 2022/3 Scientific Computing 1 (IN2005) Bader, M. Ravi, K. Wed, 10:00–12:00, MI HS2
WS 2021/2 Scientific Computing 1 (IN2005) Ravi, K.
Responsible/Coordination: Bader, M.
Wed, 10:00–12:00, MI HS2
WS 2020/1 Scientific Computing 1 (IN2005) Jovanovic Buha, I. Ravi, K. Sarbu, P.
Responsible/Coordination: Bader, M.
Wed, 10:00–12:00, MI HS1
WS 2019/20 Scientific Computing 1 (IN2005) Bader, M. Jovanovic Buha, I. Menhorn, F. Sarbu, P. Wed, 10:00–12:00, MI HS2
WS 2018/9 Scientific Computing 1 (IN2005) Bader, M. Jovanovic Buha, I. Wed, 10:00–12:00, MI HS2
WS 2017/8 Scientific Computing 1 (IN2005)
Responsible/Coordination: Bader, M.
Wed, 10:00–12:00, MI HS2
and singular or moved dates
WS 2016/7 Scientific Computing 1 (IN2005)
Responsible/Coordination: Bader, M.
Wed, 10:00–12:00, MI HS2
and singular or moved dates
WS 2014/5 Introduction to Scientific Computing 1 (IN2005) Wed, 10:00–12:00, Interims I 102
and singular or moved dates
WS 2013/4 Introduction to Scientific Computing 1 (IN2005) Wed, 10:00–12:00, MI 00.13.009A
Mon, 16:00–18:00, MI 00.13.009A
and singular or moved dates
WS 2012/3 Introduction to Scientific Computing 1 (IN2005)
Top of page