Experimental Techniques in Physics Supported with Artificial Intelligence / Machine Learning
Experimentelle Techniken in der Physik gestützt durch Künstliche Intelligenz / Maschinelles Lernen
6 CPirregularlyEN
Prerequisites
Recommended, as written
• Knowledge of mathematics, especially statistics
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.
Learning outcomes
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.
Assessment
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.
Literature (3)
- 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
Remarks
• 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“
Source: BTU's module description · retrieved 29 Sep 2026Original