Publications

INTERNATIONAL Journal papers

K.R. Ramaswamy, G. Bottegal and P.M.J. Van den Hof. Learning linear modules in a dynamic network with missing node observations. Submitted to Automatica, 2022. [ArXiv: 2208.10995]
K.R. Ramaswamy, P.Z. Csurcsia, J. Schoukens and P.M.J. Van den Hof. A frequency domain approach for local module identification in dynamic networks. Automatica, Vol. 142, Article 110370, August 2022. [ArXiv:2105.10901] [Publink] [Published]
S.J.M. Fonken, K.R. Ramaswamy and P.M.J. Van den Hof. A scalable multi-step least squares method for network identification with unknown disturbance topology. Automatica, Vol. 141, Article 110295, July 2022. [ArXiv:2106.07548] [Publink] [Published]
K.R. Ramaswamy and P.M.J. Van den Hof. A local direct method for module identification in dynamic networks with correlated noise. IEEE Trans. Automatic Control, Vol. 66, no. 11, November 2021. [Publink] [ArXiv:1908.00976] [Author version] [Published]
K.R. Ramaswamy, G. Bottegal and P.M.J. Van den Hof. Learning linear modules in a dynamic network using regularized kernel-based methods. Automatica, Vol. 129, Article 109591, July 2021. [ArXiv:2005.06266] [Publink] [Published]
P.M.J. Van den Hof and K.R. Ramasmwamy. Learning local modules in dynamic networks. Proceedings of Machine Learning Research, Vol. 144, pp. 176-188, 2021 [Publink]
K.R. Ramaswamy, O. Leeuwenburgh, R.M. Fonseca, M.M. Siraj and P.M.J. Van den Hof. Improved sampling strategies for ensemble-based optimization. Computational Geosciences, Vol. 24, May 2020. [DOI: 10.1007/s10596-019-09914-8] [Published]

INTERNATIONAL conference papers

P.M.J. Van den Hof et al., (2024). SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification. Submitted for presentation at the 20th IFAC Symposium on System Identification, 17-19 July 2024, Boston, MA, USA. [pdf-file]
S.J.M. Fonken, K.R. Ramaswamy and P.M.J. Van den Hof (2023). Local identification in dynamic networks using a multi-step least squares method. Proc. 62nd IEEE Conf. Decision and Control, 13-15 December 2023, Marina Bay Sands, Singapore, pp. 431-436. [pdf-file] [slides]
P.M.J. Van den Hof, K.R. Ramaswamy and S.J.M. Fonken (2023). Integrating data-informativity conditions in predictor models for single module identification in dynamic networks. IFAC PapersOnLine, Vol. 56-2 (2023), pp. 2377-2382. Proc. 22nd IFAC World Congress, 9-14 July 2023, Yokohama, Japan. [pdf-file] [slides]
V.C. Rajagopal, K.R. Ramaswamy and P.M.J. Van den Hof (2021). Learning local modules in dynamic networks without prior topology information. Proc. 60th IEEE Conf. Decision and Control, December 13-15, 2021, Austin, TX, USA, pp. 840 - 845. [pdf-file]
P.M.J. Van den Hof and K.R. Ramaswamy (2021). Learning local modules in dynamic networks. In Proceedings of the 3rd Conference on Learning for Dynamics and Control, volume 144 of Proceedings of Machine Learning Research, pages 176–188. PMLR, ETH Zurich, Switzerland. [Publink]
V.C. Rajagopal, K.R. Ramaswamy and P.M.J. Van den Hof. A regularized kernel-based method for learning a module in a dynamic network with correlated noise. Proc. 59th IEEE Conf. Decision and Control, Jeju Island, Republic of Korea, 8-11 December 2020, pp. 4348 - 4353. [pdf-file] [slides] [video]
P.M.J. Van den Hof and K.R. Ramaswamy. Path-based data-informativity conditions for single module identification in dynamic networks. To appear in 59th IEEE Conf. Decision and Control, Jeju Island, Republic of Korea, 8-11 December 2020. [pdf-file] [extended version] [slides] [video]
P.M.J. Van den Hof, K.R. Ramaswamy, S. Shi and H.J. Dreef. Identifiability and data-informativity for single module identification in dynamic networks. Extended abstract, accepted for presentation at the 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS2020), 23-27 August 2021, Cambridge, UK.
P.M.J. Van den Hof and K.R. Ramaswamy. Single module identification in dynamic networks - the current status. Extended abstract, Prepr. 21st IFAC World Congress, 12-17 July 2020, Berlin, Germany. [Extended abstract] [slides] [video]
K.R. Ramaswamy, P.M.J. Van den Hof and A.G. Dankers. Generalized sensing and actuation schemes for local module identification in dynamic networks. Proc. 58th IEEE Conf. Decision and Control, Nice, France, 11-13 December 2019, pp. 5519-5524. [pdf-file] [slides][extended version]
P.M.J. Van den Hof, K.R. Ramaswamy, A.G. Dankers and G. Bottegal. Local module identification in dynamic networks with correlated noise: the full input case. Proc. 58th IEEE Conf. Decision and Control, Nice, France, 11-13 December 2019, pp. 5494-5499. [pdf-file] [extended version] [slides]
K.R. Ramaswamy, G. Bottegal and P.M.J. Van den Hof. Local module identification in dynamic networks using regularized kernel-based methods. Proc. 57th IEEE Conf. Decision and Control, Miami Beach, FL, pp. 4713-4718, 17-19 December 2018 (invited paper). [pdf-file]

THesis

K.R. Ramaswamy. A guide to learning modules in a dynamic network. Doctoral dissertation, Eindhoven University of Technology, 3rd May 2022, Eindhoven, The Netherlands. ISBN 978-90-386-5500-0. [pdf-file]
K.R. Ramaswamy. Improved sampling for ensemble-based reservoir optimization with and without uncertainty. Master Thesis, Eindhoven University of Technology, 2017. [pdf-file]

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