3 citations · 4 across the 3 of their papers we have counts for
8 papers
Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach
Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung
In recent years there has been a considerable drive towards data-driven analysis, discovery and control of dynamical systems. To this end, operator theoretic methods, namely, Koopm…
Data Driven Online Learning of Power System Dynamics
Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung
With the advancement of sensing and communication in power networks, high-frequency real-time data from a power network can be used as a resource to develop better monitoring capab…
Koopman Operator Methods for Global Phase Space Exploration of Equivariant Dynamical Systems
Subhrajit Sinha, Sai P. Nandanoori, Enoch Yeung
In this paper, we develop the Koopman operator theory for dynamical systems with symmetry. In particular, we investigate how the Koopman operator and eigenfunctions behave under th…
Data-Driven Operator Theoretic Methods for Global Phase Space Learning
Sai Pushpak Nandanoori, Subhrajit Sinha, Enoch Yeung
In this work, we propose to apply the recently developed Koopman operator techniques to explore the global phase space of a nonlinear system from time-series data. In particular, w…
Information Transfer in Dynamical Systems and Optimal Placement of Actuators and Sensors for Control of Non-equilibrium Dynamics
Subhrajit Sinha, Umesh Vaidya, Enoch Yeung
In this paper we develop the concept of information transfer between the Borel-measurable sets for a dynamical system described by a measurable space and a non-singular transformat…
Online Learning of Dynamical Systems: An Operator Theoretic Approach
Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung
In this paper, we provide an algorithm for online computation of Koopman operator in real-time using streaming data. In recent years, there has been an increased interest in data-d…