Contents and objectives
Dynamical models play a key role in many branches of science. In engineering they have a paramount role in model-based simulation, health monitoring, control and optimization. The accuracy of the models is often crucial to their subsequent use in model-based operations.
Data-driven modeling and statistical parameter estimation are established fields for determining mathematical models of dynamical systems on the basis of measurement data from dedicated experiments.
The 4-days Spring School aims at covering fundamentals of data-driven modeling approaches ranging from experiment design, parameter estimation algorithms to model validation.
A common framework for most existing methods is introduced. The underlying theory and practical applications on real data are presented.
The school consists of a series of lectures in the mornings and computer exercises in the afternoons.
For the computer exercices, participants should bring their own laptop with the latest version of Matlab (version R2016a at least) installed with stand alone license.
The 24-hours course is eligible for scientific doctoral modules.