6 citations · 10 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
On Few Shot Learning of Dynamical Systems: A Koopman Operator Theoretic Approach
Subhrajit Sinha, Umesh Vaidya, Enoch Yeung
In this paper, we propose a novel algorithm for learning the Koopman operator of a dynamical system from a \textit{small} amount of training data. In many applications of data-driv…
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…
A data-driven method for quantifying the impact of a genetic circuit on its host
Aqib Hasnain, Subhrajit Sinha, Yuval Dorfan +12
Genetic circuits are designed to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant im…
On Computation of Koopman Operator from Sparse Data
Subhrajit Sinha, Enoch Yeung
In this paper we propose a novel approach to compute the Koopman operator from sparse time series data. In recent years there has been considerable interests in operator theoretic…