S
Shambhu N. Sharma
Researcher at Sardar Vallabhbhai National Institute of Technology, Surat
Publications - 79
Citations - 2025
Shambhu N. Sharma is an academic researcher from Sardar Vallabhbhai National Institute of Technology, Surat. The author has contributed to research in topics: Stochastic differential equation & Nonlinear system. The author has an hindex of 7, co-authored 74 publications receiving 1685 citations. Previous affiliations of Shambhu N. Sharma include Insight Enterprises & Netaji Subhas Institute of Technology.
Papers
More filters
Book ChapterDOI
The Fokker-Planck Equation
Shambhu N. Sharma,Hiren G. Patel +1 more
TL;DR: The Fokker-Planck equation as mentioned in this paper describes the evolution of conditional probability density for given initial states for a Markov process, which satisfies the Ito stochastic differential equation.
Journal ArticleDOI
Dynamics of a stochastically perturbed two-body problem
TL;DR: In this article, the authors used the stochastic differential equation (SDE) formalism to study the effect of such disturbances on the orbiting body, which can be modelled as a random force having Gaussian statistics.
Journal ArticleDOI
A Kolmogorov-Fokker-Planck approach for a stochastic Duffing-van der Pol system
TL;DR: In this paper, the estimation-theoretic scenarios of the stochastic version of the Duffing-van der Pol system, which accounts for a state-independent perturbation as well as a statedependent perturbations of the order n, where n ≥ 1, are investigated.
Journal ArticleDOI
Technical communique: A Kushner approach for small random perturbations of the Duffing-van der Pol system
TL;DR: This paper discusses and explores the efficacy of three non-linear filters, which are developed using the Kushner equation, for the stochastic differential system of concern here.
Journal ArticleDOI
Third-order approximate Kushner filter for a non-linear dynamical system
TL;DR: In this article, the authors derived the conditional mean and conditional covariance of the third-order approximate filter for estimating the states of a nonlinear dynamical system, especially accounting state-dependent and state-independent noise perturbations, and made a comparison of this filter with second-order Gaussian filter discussed in standard textbooks on nonlinear filtering.