For the 2023 IFAC World Congress in Yokohama, Japan, an Open Invited Track is organized on the theme:
Data-driven modeling and learning in dynamic networks
Open Invited Track Code: 9v3i4.
In this track, contributions will be collected concerning all aspects related to identification, data-driven modeling, machine learning and diagnostics 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, diagnostics 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
- Diagnostics and fault detection 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
- Mechatronic systems
When submitting a contribution to this Open Invited Track, authors are requested to refer to the Open Invited Track Code: 9v3i4.
The organizers,
Paul Van den Hof, Eindhoven University of Technology
Donatello Materassi, University of Minnesota
Shengling Shi, Delft University of Technology