IFAC2026 Open Invited Track
For the 2026 IFAC World Congress in Busan, Republic of Korea, an Open Invited Track is organized on the theme:
Data-driven modeling and learning in dynamic networks
Open Invited Track Code: 2x53k
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
- Networks of port-Hamiltonian systems
- 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
- Inference of causal relationships, Granger causality
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: 2x53k
The organizers,
Paul Van den Hof, Eindhoven University of Technology, p.m.j.vandenhof@tue.nl
Donatello Materassi, University of Minnesota, mater013@umn.edu