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Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267)

Course 0000004094 in SS 2020

General Data

Course Type practical training
Semester Weekly Hours 6 SWS
Organisational Unit Informatics 5 - Chair of Scientific Computing (Prof. Bungartz)
Lecturers Felix Dietrich
Michael Rippl
Dates Wed, 14:00–16:00, MI 00.08.038
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 Human crowd movement is comprised of highly complex dynamical systems, with hundreds or even thousands of acting and reacting participants that show several emergent behavior patterns. The scientific understanding of crowds involves topics ranging from mathematical modeling via algorithm development for machine learning (ML) from experimental and simulated data as well as implementation of simulation software up to research in psychology and sociology. Participants in this lab course will learn about the core mechanics in human movement and interactions in crowds. The current state of the art in mathematical modeling will be discussed, and students will implement models in several exercises. As a reference, the students will be introduced to the crowd simulation software VADERE (www.vadere.org). After this introduction to modeling of crowds, we will discuss current machine learning approaches to analyze the simulated results, as well as experimental data. Techniques from statistics, dynamical systems theory, manifold learning, and numerical analysis will be introduced in short lectures, implemented by the students, and then used by them to analyze their own simulation results from previous exercises. In a final project, the participants can choose to focus on their own model of a crowd, a specific aspect of crowd simulation, or a particular technique in ML to analyze simulation or experimental data. All exercises and the final project are designed for groups of three to four students. This results in a course size of about 30 students, which can be extended up to 40 by increasing the group size. The lab course covers the following topics: • Introduction to the modelling of crowds • Introduction to dynamical systems theory • Introduction to appropriate ML techniques • Numerical analysis of complex systems • Implementation of simulation software extensions and validation of models to data • Implementing ML techniques with application to simulation software results
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
WS 2020/1 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Gratl, F.
Responsible/Coordination: Bungartz, H.
Fri, 12:00–14:00, MI 02.07.023
WS 2020/1 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) -, K. Dietrich, F. Thu, 12:00–14:00, MI 02.07.023
and singular or moved dates
SS 2020 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Dorozhinskii, R. Rannabauer, L. Sarbu, P.
Responsible/Coordination: Bungartz, H.
Fri, 10:00–12:00, MI 02.07.023
and singular or moved dates
WS 2019/20 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Gratl, F. Sarbu, P. Seckler, S.
Responsible/Coordination: Bungartz, H.
Fri, 12:00–14:00, MI 02.07.023
WS 2019/20 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Dietrich, F. Gratl, F. Thu, 12:00–14:00, MI 02.07.023
and singular or moved dates
SS 2019 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Rannabauer, L. Sarbu, P. Uphoff, C.
Responsible/Coordination: Bungartz, H.
Fri, 10:00–12:00, MI 02.07.023
and singular or moved dates
WS 2018/9 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Gratl, F. Wittmann, R.
Responsible/Coordination: Bungartz, H.
SS 2018 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Rannabauer, L. Uphoff, C.
Responsible/Coordination: Bungartz, H.
WS 2017/8 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Seckler, S. Tchipev, N.
Responsible/Coordination: Bungartz, H.
SS 2017 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Rettenberger, S. Uphoff, C.
Responsible/Coordination: Bungartz, H.
WS 2016/7 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012, IN4085) Tchipev, N.
Responsible/Coordination: Bungartz, H.
SS 2016 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085)
WS 2015/6 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012, IN4085)
SS 2015 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085)
WS 2014/5 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012, IN4085)
SS 2014 Bachelor Practical Course - Scientific Computing (IN0012,IN4085)
WS 2013/4 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012, IN4085)
SS 2013 Bachelor Practical Course - Scientific Computing (IN0012,IN4085)
WS 2012/3 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012, IN4085)
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