20 citations · 33 across the 9 of their papers we have counts for
22 papers
Data-Driven Observability Decomposition with Koopman Operators for Optimization of Output Functions of Nonlinear Systems
Shara Balakrishnan, Aqib Hasnain, Robert Egbert +1
When complex systems with nonlinear dynamics achieve an output performance objective, only a fraction of the state dynamics significantly impacts that output. Those minimal state d…
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…
The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators
Shara Balakrishnan, Aqib Hasnain, Rob Egbert +1
Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output m…
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…
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…
Prediction of fitness in bacteria with causal jump dynamic mode decomposition
Shara Balakrishnan, Aqib Hasnain, Nibodh Boddupalli +3
In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-d…