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

Lehrveranstaltung 0000001145 im WS 2020/1

Basisdaten

LV-Art Praktikum
Umfang 6 SWS
betreuende Organisation Informatik 5 - Lehrstuhl für Scientific Computing (Prof. Bungartz)
Dozent(inn)en Felix Dietrich
Kislaya Ravi
Termine Do, 12:00–14:00, MI 02.07.023
sowie 2 einzelne oder verschobene Termine

Zuordnung zu Modulen

weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise 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 the 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 the 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 modeling 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-Kurs (z. B. Moodle)
TUMonline-Eintrag

Gleiche Lehrveranstaltungen (z. B. in anderen Semestern)

SemesterTitelDozent(en)Termine
SS 2024 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Gratl, F. Mishra, M. Mühlhäußer, M.
Leitung/Koordination: Bungartz, H.
Fr, 12:00–14:00, MI 00.13.054
sowie einzelne oder verschobene Termine
SS 2024 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Burak, I. Datar, C. Dietrich, F. Gaddameedi, K. Sun, Q. … (insgesamt 6) Mo, 12:00–14:00, MI 00.08.038
WS 2023/4 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Mühlhäußer, M. Newcome, S.
Leitung/Koordination: Bungartz, H.
Fr, 12:00–14:00, MI 02.07.023
WS 2023/4 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Burak, I. Datar, C. Sun, Q.
Leitung/Koordination: Dietrich, F.
Do, 12:00–14:00, MI 02.07.023
SS 2023 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Bolager, E. Burak, I. Datar, C. Dietrich, F. Mo, 12:00–14:00, MI 00.08.038
WS 2022/3 Bachelor Practical Course - Scientific Computing: Molecular Dynamics (IN0012,IN4229) Gratl, F. Newcome, S.
Leitung/Koordination: Bungartz, H.
Fr, 12:00–14:00, MI 02.07.023
WS 2022/3 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Burak, I. Dietrich, F. Do, 12:00–14:00, MI 02.07.023
SS 2022 M. Sc. Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Dietrich, F. Huang, Q. Ravi, K. Mi, 14:00–16:00, MI 00.08.038
WS 2021/2 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4229) Gratl, F.
Leitung/Koordination: Bungartz, H.
Fr, 12:00–14:00, MI 02.07.023
WS 2021/2 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Dietrich, F. Ravi, K. Do, 12:00–14:00, MI 02.07.023
SS 2021 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Dorozhinskii, R. Menhorn, F. Samfaß, P.
Leitung/Koordination: Bungartz, H.
Fr, 10:00–12:00, virtuell
SS 2021 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Dietrich, F. Ravi, K. Sarbu, P. Mi, 14:00–16:00, virtuell
WS 2020/1 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4229) Gratl, F.
Leitung/Koordination: Bungartz, H.
Fr, 12:00–14:00, virtuell
SS 2020 Bachelor Practical Course - Scientific Computing: Game Physics (IN0012,IN4085) Dorozhinskii, R. Rannabauer, L. Sarbu, P.
Leitung/Koordination: Bungartz, H.
Fr, 10:00–12:00, MI 02.07.023
sowie einzelne oder verschobene Termine
SS 2020 Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) Dietrich, F. Mi, 14:00–16:00, MI 00.08.038
sowie einzelne oder verschobene Termine
WS 2019/20 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4229) Gratl, F. Sarbu, P.
Leitung/Koordination: Bungartz, H.
Fr, 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. Do, 12:00–14:00, MI 02.07.023
sowie einzelne oder verschobene Termine
SS 2019 B.Sc. Praktikum - Scientific Computing: 3D Game Physics (IN0012, IN4085) Rannabauer, L. Sarbu, P.
Leitung/Koordination: Bungartz, H.
Fr, 10:00–12:00, MI 02.07.023
sowie einzelne oder verschobene Termine
WS 2018/9 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4229) Gratl, F.
Leitung/Koordination: Bungartz, H.
SS 2018 B.Sc. Praktikum - Scientific Computing: 3D Game Physics (IN0012, IN4085) Rannabauer, L.
Leitung/Koordination: Bungartz, H.
WS 2017/8 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4229)
Leitung/Koordination: Bungartz, H.
SS 2017 Bachelor-Praktikum - Scientific Computing: 3D Game Physics (IN0012, IN4085)
Leitung/Koordination: Bungartz, H.
WS 2016/7 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4085)
Leitung/Koordination: Bungartz, H.
SS 2016 Bachelor-Praktikum - Scientific Computing: Game Physics (IN0012, IN4085)
WS 2015/6 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4085)
SS 2015 Bachelor-Praktikum - Scientific Computing: Game Physics (IN0012, IN4085)
WS 2014/5 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4085)
SS 2014 Bachelor-Praktikum - Scientific Computing: Game Physics (IN0012, IN4085)
WS 2013/4 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4085)
SS 2013 Bachelor-Praktikum - Scientific Computing: Game Physics (IN0012, IN4085)
WS 2012/3 Bachelor-Praktikum - Scientific Computing (PSE) Molekulardynamik (IN0012, IN4085)
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