Application of machine learning and deep learning in finite element analysis: a comprehensive review
Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …
ranging from spam detection to space exploration, as a result of the boom in available data …
Generative adversarial network based data augmentation for CNN based detection of Covid-19
Covid-19 has been a global concern since 2019, crippling the world economy and health.
Biological diagnostic tools have since been developed to identify the virus from bodily fluids …
Biological diagnostic tools have since been developed to identify the virus from bodily fluids …
Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation
Background and objective Covid-19 infections are spreading around the globe since
December 2019. Several diagnostic methods were developed based on biological …
December 2019. Several diagnostic methods were developed based on biological …
Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics
SB Tandale, M Stoffel - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
The present study aims to introduce an AI algorithm suitable for neuromorphic computing to
solve Boundary Value Problems in Engineering Mechanics. Following the trend of …
solve Boundary Value Problems in Engineering Mechanics. Following the trend of …
Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
In the current study, we present an application to the class of deep learning known as the
Physics Informed Neural Networks (PINNs), more specifically we develop a new implicit …
Physics Informed Neural Networks (PINNs), more specifically we develop a new implicit …
A review of human cornea finite element modeling: geometry modeling, constitutive modeling, and outlooks
G Pang, C Wang, X Wang, X Li, Q Meng - Frontiers in Bioengineering …, 2024 - frontiersin.org
The cornea is a vital tissue of the human body. The health status of the cornea has a great
impact on the quality life of person. There has been a great deal of research on the human …
impact on the quality life of person. There has been a great deal of research on the human …
Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder–decoder architecture versus LSTM versus GRU versus TCN …
SB Tandale, M Stoffel - Computational Mechanics, 2023 - Springer
The aim of the present study is to analyse and predict the structural deformations occurring
during shock tube experiments with a series of recurrent and temporal convolutional neural …
during shock tube experiments with a series of recurrent and temporal convolutional neural …
[HTML][HTML] Physics-Based Self-Learning Spiking Neural Network enhanced time-integration scheme for computing viscoplastic structural finite element response
SB Tandale, M Stoffel - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
The present study introduces a new physics-based self-learning spiking neural framework to
compute geometrically and physically nonlinear structural response. While the so-called …
compute geometrically and physically nonlinear structural response. While the so-called …
Locally assembled stiffness matrix: a novel method to obtain global stiffness matrix
X Han, X Sun, X Chen - Acta Mechanica, 2023 - Springer
A locally assembled stiffness matrix method is proposed as a novel solution process of the
global stiffness matrix, and applied to triangular meshes of the linear-elastic plane problem …
global stiffness matrix, and applied to triangular meshes of the linear-elastic plane problem …
Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners
E Parish, P Lindsay, T Shelton, J Mersch - Computational Mechanics, 2024 - Springer
We present a machine-learning strategy for finite element analysis of solid mechanics
wherein we replace complex portions of a computational domain with a data-driven …
wherein we replace complex portions of a computational domain with a data-driven …