Objective Dynamic models play a key role in many branches of science. In engineering they have a paramount role in model-based simulation, monitoring, control and optimization. The accuracy of the models is key to their subsequent use in model-based operations. With the growing spatial complexity of engineering systems, e.g. in power networks, transportation networks and industrial production systems, also referred to as cyber-physical systems of systems, there is a strong need for effective modelling tools for dynamic networks, being considered as interconnected dynamic systems, whose spatial topology may change over time. Data-driven modelling and statistical parameter estimation are established fields for estimating models of dynamical systems on the basis of measurement data from dedicated experiments. The currently available methods, however, are limited to relatively simple structures, as open-loop or closed-loop (controlled) system configurations.In this project I will make the fundamental step towards data-driven modelling (identification) methods for dynamic networks by developing a comprehensive theory with the target to identify local dynamical models as well as the interconnection structure of the network. I will incorporate the selection of sensing and excitation locations, data synchronization, and the optimal accuracy of estimated models in view of their use for distributed control. Solving these problems is by far beyond the current abilities of the existing identification frameworks in the systems and control community. My internationally recognized expertise in the field of system identification and model-based control, together with recent work on dynamic networks, warrants the feasibility of the project.Identification methods for dynamic networks will become essential tools in the high-level future ICT environment for monitoring, control and optimization of these cyber-physical systems of systems, as well as in many other domains of science. Fields of science natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statisticsnatural sciencesmathematicspure mathematicstopologynatural sciencesmathematicsapplied mathematicsdynamical systemsnatural sciencescomputer and information sciencescomputational sciencemultiphysicsnatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-ADG-2015 - ERC Advanced Grant Call for proposal ERC-2015-AdG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Host institution TECHNISCHE UNIVERSITEIT EINDHOVEN Net EU contribution € 2 499 690,00 Address GROENE LOPER 3 5612 AE Eindhoven Netherlands See on map Region Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 499 690,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all TECHNISCHE UNIVERSITEIT EINDHOVEN Netherlands Net EU contribution € 2 499 690,00 Address GROENE LOPER 3 5612 AE Eindhoven See on map Region Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 499 690,00