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 afternoon.

For the computer exercices, it is required to bring your own laptop with Matlab (version R2016a at least ) installed with stand alone license.

The 24-hours course is eligible for scientific doctoral modules.