Zobrazeno 1 - 10
of 407
pro vyhledávání: '"Sudharsan S"'
Autor:
Sudharsan S., Raja Annamalai A.
Publikováno v:
Nanotechnology Reviews, Vol 13, Iss 1, Pp 776-800 (2024)
The alloys composed of magnesium (Mg) are deemed appropriate materials for utilization in the automotive, aerospace, and medical sectors due to their exceptionally high specific strength and density. Due to the strengthening mechanisms and superior m
Externí odkaz:
https://doaj.org/article/f92edf65db64475b9c13dacb12d73b7e
Since 1970, the R\"ossler system has remained as a considerably simpler and minimal dimensional chaos serving system. Unveiling the dynamics of a system of two coupled chaotic oscillators that leads to the emergence of extreme events in the system is
Externí odkaz:
http://arxiv.org/abs/2409.15855
Publikováno v:
Eur. Phys. J. Plus, 139, 203(2024)
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 should also be given importance. In this work, we analyze the mitigation o
Externí odkaz:
http://arxiv.org/abs/2405.05994
The foremost aim of this study is to investigate the influence of time-delayed feedback on extreme events in a non-polynomial system with velocity dependent potential. To begin, we investigate the effect of this feedback on extreme events for four di
Externí odkaz:
http://arxiv.org/abs/2112.05556
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 occurrence of extreme events in a nonlinear mechanical system. Extreme eve
Externí odkaz:
http://arxiv.org/abs/2110.09304
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 effectiveness of these two tools in suppressing extreme events in two well-
Externí odkaz:
http://arxiv.org/abs/2108.07696
We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network and Long Short-Term Memory. The Deep Learning models are tr
Externí odkaz:
http://arxiv.org/abs/2107.08819
Publikováno v:
Eur. Phys. J. Plus 136, 129 (2021)
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 of extreme events from the probability distribution function of the p
Externí odkaz:
http://arxiv.org/abs/2106.11062
Publikováno v:
In Materials Today Communications June 2023 35
Autor:
Immanual, R., Kannan, K., Chokkalingam, B., Priyadharshini, B., Sathya, J., Sudharsan, S., Raghu Nath, E.
Publikováno v:
In Materials Today: Proceedings 2023 72 Part 1:430-440