Analysis and prediction of human mobility in the United States during the early stages of the COVID-19 pandemic using regularized linear models
Since the United States started grappling with the COVID-19 pandemic, with the highest
number of confirmed cases and deaths in the world as of August 2020, most states have …
number of confirmed cases and deaths in the world as of August 2020, most states have …
On quantification and maximization of information transfer in network dynamical systems
Abstract Information flow among nodes in a complex network describes the overall cause-
effect relationships among the nodes and provides a better understanding of the …
effect relationships among the nodes and provides a better understanding of the …
Data-driven operator theoretic methods for global phase space learning
In this work, we developed new Koopman operator techniques to explore the global phase
space of a nonlinear system from time-series data. In particular, we address the problem of …
space of a nonlinear system from time-series data. In particular, we address the problem of …
On information transfer in discrete dynamical systems
In this paper, we propose a new definition of information transfer in a discrete dynamical
system. The information transfer is based on how much entropy (uncertainty) is transferred …
system. The information transfer is based on how much entropy (uncertainty) is transferred …
Causality preserving information transfer measure for control dynamical system
In this paper, we show through examples, how the existing definitions of information transfer,
namely directed information and transfer entropy fail to capture true causal interaction …
namely directed information and transfer entropy fail to capture true causal interaction …
Data-driven operator theoretic methods for phase space learning and analysis
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 subsets in …
space of a dynamical system. We defined conditions for discovering new invariant subsets in …
Data-driven distributed learning of multi-agent systems: A koopman operator approach
Koopman operator theory provides a model-free and purely data-driven technique for
studying nonlinear dynamical systems. Since the Koopman operator is infinite-dimensional …
studying nonlinear dynamical systems. Since the Koopman operator is infinite-dimensional …
Causal Analysis and Prediction of Human Mobility in the US during the COVID-19 Pandemic
Since the increasing outspread of COVID-19 in the US, with the highest number of confirmed
cases and deaths in the world as of September 2020, most states in the country have …
cases and deaths in the world as of September 2020, most states in the country have …
Identifying causal interaction in power system: Information-based approach
Stability analysis of power system is a problem of immense importance in power community.
Identification of the cause for instability is a relevant problem and has been studied widely …
Identification of the cause for instability is a relevant problem and has been studied widely …
On few shot learning of dynamical systems: A koopman operator theoretic approach
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 …
dynamical system from a\textit {small} amount of training data. In many applications of data …