S
Sudip Dey
Researcher at National Institute of Technology, Silchar
Publications - 179
Citations - 2642
Sudip Dey is an academic researcher from National Institute of Technology, Silchar. The author has contributed to research in topics: Finite element method & Monte Carlo method. The author has an hindex of 28, co-authored 155 publications receiving 1956 citations. Previous affiliations of Sudip Dey include North Eastern Hill University & Leibniz Institute for Neurobiology.
Papers
More filters
Journal ArticleDOI
Surface ultrastructure of larval mouthpart sensilla of the muga silkmoth, Antheraea assamensis, an endemic species of North-East India.
TL;DR: Scanning electron microscopy of different larval stages of the muga silk moth Antheraea assamensis revealed the presence of sensilla chaetica, sensilla trichoidea, sensillas styloconica, gustatory sensilla, sensory pegs, placoid sensillas, etc., on their mouth parts.
Journal ArticleDOI
Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene
K. K. Gupta,Lintu Roy,Sudip Dey +2 more
TL;DR: In this paper , a polynomial chaos-Kriging-based molecular dynamics simulation framework of twisted bilayer graphene (tBLG) structures is presented to investigate the influence of stochastic parametric variations on their nano-indentation behavior.
Journal ArticleDOI
Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions
TL;DR: This first of its kind study on coronaviruses along with the proposed generic machine learning based approach will accelerate the detection of viruses and create efficient pathways toward future inventions leading to cure and containment in the field of virology.
Journal ArticleDOI
Probabilistic assessment on buckling behavior of sandwich panel:- A radial basis function approach
TL;DR: In this article, the buckling load of laminated sandwich panel is obtained by employing higher-order-zigzag theory (HOZT) coupled with RBF and probabilistic finite element (FE) model.
Book ChapterDOI
Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates
TL;DR: The prediction capability of a surrogate model (polynomial neural network) to estimate the stochastic buckling behavior of sandwich plates is presented and the constructed PNN model is found to be convergent with the results obtained by direct Monte Carlo simulation techniques.