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S.V.Manivelan|

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I'm S.V.Manivelan

Research Scholar
Department of Physics, M. R. Government Arts College (Affiliated to Bharathidasan University), Mannargudi, Tamilnadu, India - 614 001.

I am Manivelan Saminathan Vasuki, Research Scholar specializing in Nonlinear Dynamics and Chaos, delving deep into Physics through computational insights. Currently, I am investigating the statistical properties and dynamics of extreme events across various nonlinear systems. By applying chaos and Extreme Value Theory principles, I aim to deeply comprehend their occurrence and impact.

I'm ready to engage in discussions and work. For me, research is all about discovering enjoyable new things and fostering enriching connections.

Research Experience

Publications:2

h-Index: 1

Citations: 5

Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system.

S.V. Manivelan, S. Sabarathinam, K. Thamilmaran, I. Manimehan

Citation: 0

Read On: Chaos

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Dynamical instabilities cause extreme events in a theoretical Brusselator model

S.V. Manivelan, S. Sabarathinam, K. Thamilmaran, I. Manimehan

Citation: 5

Read On: Chaos, Solitons & Fractals

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Research Scholar

Year: 2023 - Present

Department of Physics

M. R. Government Arts College (Affiliated to Bharathidasan University), India.

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Present Work

Working on |
Investigating Extreme Events Occurrences in Dynamical Systems

Explores the rare but significant outliers in system behavior, analyzing their origins and impacts. This research enhances understanding of the mechanisms driving extreme events, aiding in prediction and mitigation strategies.
Exploring collective dynamics in networks

Examines how individual components interact and give rise to complex, emergent behaviors within a network. This research provides insights into the underlying principles governing synchronization, pattern formation, and robustness in various systems, from biological to technological networks.
Explore the Chimera State in Coupled Systems.

Investigates the coexistence of synchronized and desynchronized groups within the same network of interacting elements. This phenomenon providing insights into stability and transition dynamics in complex systems.
Machine Learning for Predicting Extreme Events

Leverages advanced algorithms to analyze patterns and trends in large datasets, aiming to forecast rare and significant occurrences. This approach enhances the accuracy and reliability of predictions in various fields, including finance, weather, and engineering, ultimately aiding in better preparedness and risk management.