Forår Lectures will be given in the period between 3 - 7 August, 2009.
Undervisningsform:
About one week of teaching and computer exercises, followed by two or more weeks where the methods are applied to the student’s own model or system. Lectures will typically take place in the morning, and will be followed by practical sessions in the afternoon. Later on, students are to apply the methods to a model/system agreed between student and teachers (as a case study format). The evaluation is based on the submission of a final report about the case study (including Matlab code).
Modeling is actively used in various scientific and engineering disciplines for a variety of ends: from development of process understanding to design, control and operation of (natural or man-made) systems. Most numerical models simulating such systems tend to be complex with many parameters, state-variables and non-linear relations resulting in many degrees of freedom. Using a fine-tuning method (manually or statistically), these models can be made to produce virtually any desired behavior to fit the observations about the system in question. What is challenging, however, is to ascertain a degree of reliability and credibility of the models before one applies them in reality. It is precisely the objective of this course to introduce students to modern techniques of model analysis: uncertainty and sensitivity. While sensitivity analysis has focus on how the uncertainty in the model output can be related to the uncertainty in the inputs of a model, the uncertainty analysis itself is about quantifying the uncertainty in the outputs of a model. The primary aim of this course is therefore to make the student able to analyze the uncertainty and sensitivity of models in the Matlab® computing and simulation environment. Global (contemporary) as well as local (classical) methods for uncertainty and sensitivity analysis will be covered during the course. The course aims at giving hands-on experience with the topics studied, i.e. the student will learn how to apply a method and how to interpret the results generated by this method. Therefore, lectures about the theory will be followed by exercise sessions where the methods explained in the lectures can be applied. Examples are taken from the textbook, from the literature and from ongoing research work at the Center for Bioprocess Engineering and CAPEC.
Læringsmål:
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
Quantify and interpret uncertainty in the model outputs using Monte-Carlo technique
Quantify and interpret uncertainty in the model outputs using linear error propagation
Perform and interpret sensitivity analysis using (i) differentiation, (ii) regression, (iii) variance, and (iv) Monte Carlo filtering based techniques
Apply and evaluate uncertainty and sensitivity analysis to linear and non-linear type numerical models
Litteratur:
Textbook: Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global Sensitivity Analysis: The primer. West Sussex, England, John Wiley & Sons, 2008.
Bemærkninger:
Knowledge of Matlab at the start of the course is an advantage, but not a requirement. Assuming that you have access to Matlab, an introduction to Matlab with exercises can be sent to you a couple of weeks before the start of the course.
