Data-driven Model Learning of Dynamic Systems

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 (system identification) and statistical parameter estimation are established fields for determining mathematical models of dynamical systems on the basis of measurement data from dedicated experiments.

The 5-days Spring School aims at covering the fundamentals of data-driven modeling approaches (ranging from parameter estimation algorithms (PEM and ETFE) and experiment design to model validation) as well as more advanced topics. In this year edition, these advanced topics will pertain to closed-loop identification, to optimal experiment design and to the use of statistical tools (such as the maximum likelihood theory) for learning the dynamics of linear and nonlinear systems.

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 (system identification) and statistical parameter estimation are established fields for determining mathematical models of dynamical systems on the basis of measurement data from dedicated experiments.

The 5-days Spring School aims at covering the fundamentals of data-driven modeling approaches (ranging from parameter estimation algorithms (PEM and ETFE) and experiment design to model validation) as well as more advanced topics. In this year edition, these advanced topics will pertain to closed-loop identification, to optimal experiment design and to the use of statistical tools (such as the maximum likelihood theory) for learning the dynamics of linear and nonlinear systems.