3 citations · 8 across the 26 of their papers we have counts for
4 papers · 1 filter
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach
S. Sinha, Sai P. Nandanoori, David Barajas-Solano
Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological n…
Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis
Sai Pushpak Nandanoori, Subhrajit Sinha, Enoch Yeung
This paper uses data-driven operator theoretic approaches to explore the global phase space of a dynamical system. We defined conditions for discovering new invariant subspaces in…
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…