23 citations · 75 across the 10 of their papers we have counts for
10 papers
Controlling extreme events in neuronal networks: A single driving signal approach
R. Shashangan, S. Sudharsan, Dibakar Ghosh +1
We show that in a drive-response coupling framework extreme events are suppressed in the response system by the dominance of a single driving signal. We validate this approach acro…
Propagation of extreme events in multiplex neuronal networks
R. Shashangan, S. Sudharsan, Dibakar Ghosh +1
In previous studies, the propagation of extreme events across nodes in monolayer networks has been extensively studied. In this work, we extend this investigation to explore the pr…
Extreme events in two-coupled chaotic oscillators
S. Sudharsan, Tapas Kumar Pal, Dibakar Ghosh +1
Since 1970, the Rössler system has remained as a considerably simpler and minimal dimensional chaos serving system. Unveiling the dynamics of a system of two coupled chaotic oscill…
Intermittent cluster synchronization in a unidirectional ring of bursting neurons
Ardhanareeswaran R Sree, Sudharsan S, Senthilvelan M +1
We report a new mechanism through which extreme events with a dragon king-like distribution emerge in a network of unidirectional ring of Hindmarsh-Rose bursting neurons interactin…
Mitigation of extreme events in an excitable system
R. Shashangan, S. Sudharsan, A. Venkatesan +1
Formulating mitigation strategies is one of the main aspect in the dynamical study of extreme events. Apart from the effective control, easy implementation of the devised tool shou…
Prediction of Occurrence of Extreme Events using Machine Learning
J. Meiyazhagan, S. Sudharsan, A. Venkatasen +1
Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occu…
Constant Bias and Weak Second Periodic Forcing : Tools to Mitigate Extreme Events
S. Sudharsan, A. Venkatesan, M. Senthilvelan
We propose two potentially viable non-feedback methods, namely (i) constant bias and (ii) weak second periodic forcing as tools to mitigate extreme events. We demonstrate the effec…
Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning
J. Meiyazhagan, S. Sudharsan, M. Senthilvelan
We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neur…
Emergence and mitigation of extreme events in a parametrically driven system with velocity-dependent potential
S. Sudharsan, A. Venkatesan, P. Muruganandam +1
In this paper, we discuss the emergence of extreme events in a parametrically driven non-polynomial mechanical system with a velocity-dependent potential. We confirm the occurrence…
Symmetrical emergence of extreme events at multiple regions in a damped and driven velocity-dependent mechanical system
Sudharsan S, Venkatesan A, Senthilvelan M
In this work, we report the emergence of extreme events in a damped and driven velocity-dependent mechanical system. We observe that the extreme events emerge at multiple points. W…