29 citations · 97 across the 24 of their papers we have counts for
30 papers
Learning Invariant Subspaces of Koopman Operators--Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance
Charles A. Johnson, Shara Balakrishnan, Enoch Yeung
This work builds on the models and concepts presented in part 1 to learn approximate dictionary representations of Koopman operators from data. Part I of this paper presented a met…
Learning Invariant Subspaces of Koopman Operators--Part 1: A Methodology for Demonstrating a Dictionary's Approximate Subspace Invariance
Charles A. Johnson, Shara Balakrishnan, Enoch Yeung
Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and i…
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…
Heterogeneous mixtures of dictionary functions to approximate subspace invariance in Koopman operators
Charles A. Johnson, Shara Balakrishnan, Enoch Yeung
Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and i…
On the Stability, Economic Efficiency and Incentive Compatibility of Electricity Market Dynamics
Pengcheng You, Yan Jiang, Enoch Yeung +2
This paper focuses on the operation of an electricity market that accounts for participants that bid at a sub-minute timescale. To that end, we model the market-clearing process as…
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…
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…
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…
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm
Zhiyuan Liu, Guohui Ding, Lijun Chen +1
We propose an alternating optimization algorithm to the nonconvex Koopman operator learning problem for nonlinear dynamic systems. We show that the proposed algorithm will converge…
Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition
Aqib Hasnain, Nibodh Boddupalli, Shara Balakrishnan +1
This paper describes the optimal selection of a control policy to program the steady state of controlled nonlinear systems with hyperbolic fixed points. This work is motivated by t…
Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems
Nibodh Boddupalli, Aqib Hasnain, Sai Pushpak Nandanoori +1
It is hard to identify nonlinear biological models strictly from data, with results that are often sensitive to experimental conditions. Automated experimental workflows and liquid…
Optimal reporter placement in sparsely measured genetic networks using the Koopman operator
Aqib Hasnain, Nibodh Boddupalli, Enoch Yeung
Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design…
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…
A Constructive Approach for One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings
W. Brent Daniel, Enoch Yeung
In this paper we presented a novel constructive approach for training deep neural networks using geometric approaches. We show that a topological covering can be used to define a c…
Decomposition of Nonlinear Dynamical Networks via Comparison Systems
Abdullah Maruf, Soumya Kundu, Enoch Yeung +1
In analysis and control of large-scale nonlinear dynamical systems, a distributed approach is often an attractive option due to its computational tractability and usually low commu…
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky +1
We suggest ways to enforce given constraints in the output of a Generative Adversarial Network (GAN) generator both for interpolation and extrapolation (prediction). For the case o…