Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Md. Imrul Reza Shishir"'
Publikováno v:
Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters.
In this present work, a neural network (NN) is trained to deal with the optimization process of topology optimization and generate optimized structures. The NN’s activation functions are used to represent the popular Solid Isotropic Material with P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0ed16eec8b8aab649c1aaf4e808a694
https://doi.org/10.1115/imece2022-94604
https://doi.org/10.1115/imece2022-94604
Publikováno v:
Computational Materials Science. 218:111924
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee8f6d370af1c9deca3063e472132c49
https://doi.org/10.1016/j.commatsci.2022.111924
https://doi.org/10.1016/j.commatsci.2022.111924
Publikováno v:
Volume 12: Mechanics of Solids, Structures, and Fluids; Micro- and Nano- Systems Engineering and Packaging.
A machine learning model is developed to predict the crack propagation path in polycrystalline graphene sheets. The dataset used for training the machine learning (ML) model is obtained from the molecular dynamics (MD) simulations. A training set of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0779407c0a404a73d23a4a4942a9caf
https://doi.org/10.1115/imece2021-70543
https://doi.org/10.1115/imece2021-70543
Publikováno v:
Volume 12: Mechanics of Solids, Structures, and Fluids; Micro- and Nano- Systems Engineering and Packaging.
A data-driven deep convolution neural network model is used to predict the fracture properties of polycrystalline graphene from the atomic resolution image. A large dataset is prepared using molecular dynamic simulations and atomic resolution image o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54eb678556ef46b144703d671c4d046c
https://doi.org/10.1115/imece2021-70580
https://doi.org/10.1115/imece2021-70580
Publikováno v:
Physical chemistry chemical physics : PCCP. 23(26)
Molecular dynamics simulations are used to extract the traction-separation laws (TSLs) of symmetric grain boundaries of graphene. Grain boundaries with realistic atomic structures are constructed using different types of dislocations. The TSLs of gra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::663396d3836cbbf781415c19031b838d
https://pubmed.ncbi.nlm.nih.gov/34160495
https://pubmed.ncbi.nlm.nih.gov/34160495
Publikováno v:
Computational Materials Science. 201:110878
A machine learning model is proposed to predict the brittle fracture of polycrystalline graphene under tensile loading. The model employs a convolutional neural network, bidirectional recurrent neural network, and fully connected layer to process the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7422f5c73792aac6305f96cbf659965
https://doi.org/10.1016/j.commatsci.2021.110878
https://doi.org/10.1016/j.commatsci.2021.110878
Publikováno v:
Mechanics of Materials. 160:103895
Molecular dynamics simulations are used to study the mechanical and fracture properties of C3N. The impact of initial crack orientation on the crack path is studied by applying tensile strain to C3N sheets containing initial cracks in the armchair an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d18197bf823777466dc4ede707333463
https://doi.org/10.1016/j.mechmat.2021.103895
https://doi.org/10.1016/j.mechmat.2021.103895
Autor:
Asma Akther, Abdullahil Kafy, Lindong Zhai, Hyun Chan Kim, MD Imrul Reza Shishir, Jaehwan Kim
Publikováno v:
Smart Materials & Structures; Nov2016, Vol. 25 Issue 11, p1-1, 1p