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20172020
most citedData-Driven Approximation of Transfer Operators: Naturally Structured Dynamic Mode Decomposition

10 citations · 20 across the 8 of their papers we have counts for

collaborators

9 papers

eess.SY20205 cited

A convex data-driven approach for nonlinear control synthesis

Hyungjin Choi, Umesh Vaidya, Yongxin Chen

We consider a class of nonlinear control synthesis problems where the underlying mathematical models are not explicitly known. We propose a data-driven approach to stabilize the sy…

eess.SY2020

Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems

Amarsagar Reddy Ramapuram Matavalam, Umesh Vaidya, Venkataramana Ajjarapu

In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of…

eess.SY20191 cited

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…

math.OC20192 cited

Data-Driven Nonlinear Stabilization Using Koopman Operator

Bowen Huang, Xu Ma, Umesh Vaidya

We propose the application of Koopman operator theory for the design of stabilizing feedback controller for a nonlinear control system. The proposed approach is data-driven and rel…

math.DS2019

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…

cs.LG20171 cited

Particle Clustering Machine: A Dynamical System Based Approach

Sambarta Dasgupta, Keivan Ebrahimi, Umesh Vaidya

Identification of the clusters from an unlabeled data set is one of the most important problems in Unsupervised Machine Learning. The state of the art clustering algorithms are bas…