SYSDYNET SIGNED
Data-driven Modelling in Dynamic Networks
Total Cost €
0EC-Contrib. €
0Partnership
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0SYSDYNET project word cloud
Explore the words cloud of the SYSDYNET project. It provides you a very rough idea of what is the project "SYSDYNET" about.
Project "SYSDYNET" data sheet
The following table provides information about the project.
| Coordinator |
TECHNISCHE UNIVERSITEIT EINDHOVEN
Organization address contact info |
| Coordinator Country | Netherlands [NL] |
| Project website | http://www.sysdynet.eu |
| Total cost | 2˙499˙690 € |
| EC max contribution | 2˙499˙690 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2015-AdG |
| Funding Scheme | ERC-ADG |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-09-01 to 2021-08-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | TECHNISCHE UNIVERSITEIT EINDHOVEN | NL (EINDHOVEN) | coordinator | 2˙499˙690.00 |
Map
Project 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.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof Bayesian topology identification of linear dynamic networks published pages: 2814-2819, ISSN: , DOI: 10.23919/ecc.2019.8795766 |
2019 18th European Control Conference (ECC) | 2019-10-30 |
| 2017 |
P.M.J Van den Hof, A.G. Dankers, H.H.M. Weerts Identification in dynamic networks published pages: , ISSN: , DOI: |
Proc. Foundations of Computer Aided Process Operations / Chemical Process Control FOCAPO/CPC 2017 | 2019-10-29 |
| 2018 |
Hof, Paul M. J. Van den; Ramaswamy, Karthik R.; Dankers, Arne G.; Bottegal, Giulio Local module identification in dynamic networks with correlated noise: the full input case published pages: , ISSN: , DOI: |
Technical Report 4 | 2019-10-29 |
| 2018 |
Karthik R. Ramaswamy, Giulio Bottegal, Paul M.J. Van den Hof Local Module Identification in Dynamic Networks Using Regularized Kernel-Based Methods published pages: 4713-4718, ISSN: 9781-5386, DOI: 10.1109/cdc.2018.8619436 |
2018 IEEE Conference on Decision and Control (CDC) | 2019-10-29 |
| 2018 |
Harm Weerts, Paul M.J. Van den Hof, Arne Dankers Single Module Identifiability in Linear Dynamic Networks published pages: 4725-4730, ISSN: 9781-5386, DOI: 10.1109/cdc.2018.8619365 |
2018 IEEE Conference on Decision and Control (CDC) | 2019-10-29 |
| 2018 |
H.H.M. Weerts Identifiability and identification methods for dynamic networks published pages: , ISSN: , DOI: |
MSc Thesis Report | 2019-10-29 |
| 2018 |
Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof Bayesian topology identification of linear dynamic networks published pages: , ISSN: , DOI: |
Technical Report | 2019-10-29 |
| 2017 |
E.E. Raclaru Topology detection using Bayesian statistics published pages: , ISSN: , DOI: |
MSc Thesis Report | 2019-10-29 |
| 2019 |
Harm H.M. Weerts, Jonas Linder, Martin Enqvist, Paul M.J. Van den Hof Abstractions of linear dynamic networks for input selection in local module identification published pages: , ISSN: , DOI: |
Technical Report | 2019-10-29 |
| 2018 |
Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof Bayesian topology identification of linear dynamic networks published pages: , ISSN: , DOI: |
Technical Report | 2019-10-09 |
| 2017 |
Harm H.M. Weerts, Paul M.J. Van den Hof, Arne G. Dankers Identification of dynamic networks with rank-reduced process noise * *This work has received funding from the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694504). published pages: 10562-10567, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2017.08.1319 |
IFAC-PapersOnLine 50/1 | 2019-10-09 |
| 2018 |
M. Schoukens, J.P. Noël, P.M.J. Van den Hof Combining Experiments for Linear Dynamic Network Identification in the Presence of Nonlinearities published pages: 212026, ISSN: 1742-6588, DOI: 10.1088/1742-6596/1065/21/212026 |
Journal of Physics: Conference Series 1065 | 2019-10-09 |
| 2017 |
P.M.J Van den Hof, A.G. Dankers, H.H.M. Weerts Identification in dynamic networks published pages: , ISSN: , DOI: |
Proc. Foundations of Computer Aided Process Operations / Chemical Process Control FOCAPO/CPC 2017 | 2019-10-09 |
| 2018 |
Tom R.V. Steentjes, Mircea Lazar, Paul M.J. Van den Hof A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems published pages: 236-241, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2018.12.041 |
IFAC-PapersOnLine 51/23 | 2019-10-09 |
| 2017 |
Arne Dankers, Paul M.J. Van den Hof, Donatello Materassi, Harm H.M. Weerts Conditions for handling confounding variables in dynamic networks * *The work of A. Dankers is supported by Mitacs of Canada. The work of P. Van den Hof and H. Weerts is supported by the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694504). published pages: 3983-3988, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2017.08.771 |
IFAC-PapersOnLine 50/1 | 2019-06-13 |
| 2018 |
Giulio Bottegal, Alessandro Chiuso, Paul M.J. Van den Hof On dynamic network modeling of stationary multivariate processes published pages: 850-855, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2018.09.118 |
IFAC-PapersOnLine 51/15 | 2019-06-13 |
| 2018 |
Harm H.M. Weerts, Paul M.J. Van den Hof, Arne G. Dankers Prediction error identification of linear dynamic networks with rank-reduced noise published pages: 256-268, ISSN: 0005-1098, DOI: 10.1016/j.automatica.2018.09.033 |
Automatica 98 | 2019-06-13 |
| 2018 |
E.M.M. Kivits, Paul M.J. Van den Hof On Representations of Linear Dynamic Networks published pages: 838-843, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2018.09.120 |
IFAC-PapersOnLine 51/15 | 2019-06-13 |
| 2018 |
Paul M.J. Van den Hof, Arne G. Dankers, Harm H.M. Weerts Identification in dynamic networks published pages: 23-29, ISSN: 0098-1354, DOI: 10.1016/j.compchemeng.2017.10.005 |
Computers & Chemical Engineering 109 | 2019-06-13 |
| 2018 |
Harm H.M. Weerts, Miguel Galrinho, Giulio Bottegal, HÃ¥kan Hjalmarsson, Paul M.J. Van den Hof A sequential least squares algorithm for ARMAX dynamic network identification published pages: 844-849, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2018.09.119 |
IFAC-PapersOnLine 51/15 | 2019-06-13 |
| 2018 |
Niklas Everitt, Giulio Bottegal, HÃ¥kan Hjalmarsson An empirical Bayes approach to identification of modules in dynamic networks published pages: 144-151, ISSN: 0005-1098, DOI: 10.1016/j.automatica.2018.01.011 |
Automatica 91 | 2019-06-13 |
| 2018 |
Harm H.M. Weerts, Paul M.J. Van den Hof, Arne G. Dankers Identifiability of linear dynamic networks published pages: 247-258, ISSN: 0005-1098, DOI: 10.1016/j.automatica.2017.12.013 |
Automatica 89 | 2019-06-13 |
| 2018 |
M. Schoukens, P.M.J. Van den Hof Detecting Nonlinear Modules in a Dynamic Network: A Step-by-Step Procedure published pages: 593-597, ISSN: 2405-8963, DOI: 10.1016/j.ifacol.2018.09.224 |
IFAC-PapersOnLine 51/15 | 2019-06-13 |
| 2018 |
H.H.M. Weerts Identifiability and identification methods for dynamic networks published pages: , ISSN: , DOI: |
2019-05-27 |
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