IFAC2020 Open Invited Track

For the 2020 IFAC World Congress in Berlin, Germany, an Open Invited Track is organized on the theme:

Data-driven modeling and learning in dynamic networks

In this track, contributions will be collected concerning all aspects related to identification, data-driven modeling and machine learning in systems that are structurally interconnected in dynamic networks. This includes  aspects of modeling, representations, analysis and model reduction of dynamic networks, as well as of (data-driven) learning and control of networks.
We solicit contributions both in theory, new methods and algorithms, as well as in applications.

Particular subjects of interest are:

  • Local module identification
  • Machine learning approaches to modeling structured systems
  • Network identifiability
  • Sparse topology estimation
  • Experiment design and signal allocation problems
  • Physical networks and network analysis
  • Model reduction in networks
  • Fundamental representations of interconnected systems
  • Security aspects in networks
  • Fault detection and diagnosis in networks
  • Scalable algorithms
  • Data-driven multi-agent and distributed control
  • Distributed estimation and identification
  • Heterogeneous data
  • Hybrid networks

Applications may include:

  • Power grids
  • Biological and gene regulatory networks
  • Brain networks, neuroscience
  • Large scale systems in process control
  • Infrastructural systems,
  • Smart buildings
  • Robotic networks
  • Transportation networks

 

The organizers,

Paul Van den Hof, Eindhoven University of Technology
Alessandro Chiuso, University of Padova
Jorge Goncalves, University of Luxembourg