no code implementations • 30 Jan 2023 • Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng, Gautam Dasarathy
Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data -- the so called differential network analysis problem.
no code implementations • 10 Nov 2022 • Abrar Zahin, Rajasekhar Anguluri, Oliver Kosut, Lalitha Sankar, Gautam Dasarathy
A recent line of work establishes that even for tree-structured graphical models, only partial structure recovery is possible and goes on to devise algorithms to identify the structure up to an (unavoidable) equivalence class of trees.
no code implementations • 9 Aug 2022 • Rajasekhar Anguluri, Lalitha Sankar, Oliver Kosut
This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution.
no code implementations • 21 Jun 2022 • Nafiseh Ghoroghchian, Rajasekhar Anguluri, Gautam Dasarathy, Stark C. Draper
We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries.
1 code implementation • 14 Jun 2022 • Anirudh Rayas, Rajasekhar Anguluri, Gautam Dasarathy
Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws.
no code implementations • 29 Sep 2021 • Nafiseh Ghoroghchian, Rajasekhar Anguluri, Gautam Dasarathy, Stark Draper
In contrast, in this paper the controllability aspects of the coarse system are derived from coarse summaries {\em without} knowledge of the fine-scale structure.
no code implementations • L4DC 2020 • Rajasekhar Anguluri, Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
This paper proposes a new framework and several results to quantify the performance of data-driven state-feedback controllers for linear systems against targeted perturbations of the training data.