Prerequisite/Recommended prerequisite for
participation in the module
The module builds upon knowledge obtained in the modules Applied
Statistics and Probability Theory or similar.
Content, progress and pedagogy of the
module
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
- 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
- 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.
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