Content, progress and pedagogy of the module

The module builds upon knowledge obtained in the modules Applied Statistics and Probability Theory.

Learning objectives

Knowledge

• Understand methodology for design of experiments and test series and for reduction of ambiguity of experimental results, and for comparability with model predictions
• Explain elementary and advanced quantification tools, and their application to validation between model  and experiment data
• Account for common contemporary methods and relevant specific industry standards
• Understand processing methods for analog and digital data (continuous vs. discrete)

Skills

• Scrutinize a non-trivial physical systems for appropriate experimental study
• Isolate principal measurable parameters
• Design an experiment matrix for systematic variation of parameters using digital platforms
• Perform a probabilistic study of the experimental data in order to quantify the influence of individual parameters
• Scrutinize a model (analytical or numerical) for comparison with an appropriate experimental study using digital platforms
• Isolate principal input parameters and their known or assumed statistical variations
• Perform a probabilistic study of the model in order to quantify the level of confidence
• Account for the level of coherence between test results and model predictions
• Identify invalid data (outliers)
• Account for common errors and limitations in the processing of model data or experimentally obtained data

Competences

• Undertake experiment planning and execution for refinement and validation (or rejection) of model-based predictions of phenomena within their principal line of study

Type of instruction

The course is taught by a mixture of lectures, workshops, exercises, mini-projects and self-studies, including e-learning activities.

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.

Exam

Exams