Data-driven Model Learning of Dynamic Systems
A five-days PhD course on data-based modeling (system identification) covering both the fundamentals and more advanced topics.
Practical information:
Want to register?
- Register until: 31 Jan 2023
- Prerequisites: computer met de laatste versie van Matlab (versie R2018a)
- Price: €230 for PhD, €500 for others
Subscribe until 31 January 2023
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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.
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