activity
20192024
most citedExplosive death in nonlinear oscillators coupled by quorum sensing

29 citations · 71 across the 9 of their papers we have counts for

collaborators

10 papers

nlin.CD2024

Adaptive control in dynamical systems using reservoir computing

Swarnendu Mandal, Swati Chauhan, Umesh Kumar Verma +2

We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to p…

nlin.AO2024

Recovery of synchronized oscillations on multiplex networks by tuning dynamical time scales

Aiwin T Vadakkan, Umesh Kumar Verma, G. Ambika

The heterogeneity among interacting dynamical systems or variations in the pattern of their interactions occur naturally in many real complex systems. Often they lead to partially…

nlin.AO2023

Solitary death in coupled limit-cycle oscillators with higher-order interactions

Subhasanket Dutta, Umesh Kumar Verma, Sarika Jalan

Coupled limit cycle oscillators with pairwise interactions depict phase transitions to amplitude or oscillation death. This Letter introduces a scheme for higher-order interactions…

nlin.CD2023

Explosive death in coupled oscillators with higher-order interactions

Richita Ghosh, Umesh Kumar Verma, Sarika Jalan +1

We investigate the dynamical evolution of globally connected Stuart-Landau oscillators coupled through conjugate or dis-similar variables on simplicial complexes. We report a first…

nlin.AO2022★ 8 cited

Explosive synchronization in coupled nonlinear oscillators on multiplex network

Umesh Kumar Verma, G. Ambika

We report the emergence of explosive synchronization in a multiplex network where oscillators on the first layer are coupled with attractive coupling and those on the second layer,…

nlin.AO2021

Emergent Dynamics and Spatio Temporal Patterns on Multiplex Neuronal Networks

Umesh Kumar Verma, G. Ambika

We present a study on the emergence of a variety of spatio temporal patterns among neurons that are connected in a multiplex framework, with neurons on two layers with different fu…