13908 - Experimental Techniques in Physics Supported with Artificial Intelligence / Machine Learning Modulübersicht

Module Number: 13908
Module Title:Experimental Techniques in Physics Supported with Artificial Intelligence / Machine Learning
  Experimentelle Techniken in der Physik gestützt durch Künstliche Intelligenz / Maschinelles Lernen
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr.-Ing. Dr. rer. nat. habil. Schenk, Harald
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: On special announcement
Credits: 6
Learning Outcome:After completion of the module, students will have an overview on methods and tools used for experimentation with physical systems. They know the role of artificial intelligence/machine learning (AI/ML) in inference with experimental data processing. They are able to apply the acquired knowledge in computer experiments realized during laboratories.
Contents:The subject of the module is the fundamentals of experiment design, including the theoretical and practical aspects of experiment planning and execution. This includes, for example, the role of observation and measurement in cognition process, data collection and processing with the use of statistical and AI/ML methods, data and system modeling, computer simulation, and planning of experiment.
The laboratory will use computer simulation to solve selected problems of experimentation, e.g. forward and inverse modeling, signal reconstruction, model identification, experiment planning. Statistical and AI/ML techniques will be used in exemplary tasks. The form of the class includes the realization of tasks under supervision and solving self-defined problems.
Recommended Prerequisites:
  • Knowledge of mathematics, especially statistics
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Practical training / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:
  • Brandt S.: Data analysis. Statistical and computational methods for scientists and engineers. 4th Ed., Springer, Heidelberg 2014
  • Söderstöm T., Stoica P.: System identification. Prentice Hall, Michigan, USA, 1989
  • Lakshmanan V., Robinson S., Munn M.: Machine learning design patterns. Solutions to common challenges in data preparation, model building, and MLOps. O'Reilly, USA 2020
Module Examination:Prerequisite + Final Module Examination (MAP)
Assessment Mode for Module Examination:Prerequisite:
  • Successful completion of the project task,  30 h
Final module examination:
  • Written examination, 90 min. OR
  • Oral examination, 30-45 min. (with small number of participants)
In the first lecture it will be announced, wheter the examination will organized in written or oral form.
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:20
Part of the Study Programme:
  • Abschluss im Ausland / Artificial Intelligence / keine PO
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Master (research-oriented) / Physics / PO 2021
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex  „Knowledge Acquisition, Representation, and Processing“
  • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“
Module Components:
  • Lecture: Data exploration and system management using AI/ML
  • Accompanying laboratoy
  • Related examination
Components to be offered in the Current Semester: