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Advanced Deep Learning for Physics (IN2298)

Course 0000003205 in SS 2023

General Data

Course Type lecture
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 15 - Chair of Computer Graphics and Visualization (Prof. Westermann)
Lecturers Shuvayan Brahmachary
Liwei Chen
Philipp Holl
Björn List
Marton Szep
Susanne Weitz
Responsible/Coordination: Nils Thürey
Dates Tue, 16:00–18:00, MI HS2
Fri, 08:00–10:00, MI HS2

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 This course will give a practical and comprehensive introduction of deep learning in the context of physical simulations. Beyond standard supervised learning from data, we’ll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and improved gradient calculations. These methods have a significant potential to fundamentally change what computer simulations can achieve. As much as possible, the topics will come with hands-on code examples and exercises. More information at:
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

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