Static, dynamic and stability analysis of multi-dimensional functional graded plate with variable thickness using deep neural network

• URL: http://arxiv.org/abs/2301.05900v1
• Date: Sat, 14 Jan 2023 11:37:01 GMT
• Title: Static, dynamic and stability analysis of multi-dimensional functional graded plate with variable thickness using deep neural network
• Authors: Nam G. Luu and Thanh T. Banh
• Abstract summary: The goal of this paper is to analyze and predict the central deflection, natural frequency, and critical buckling load of the multi-directional functionally graded (FG) plate. The material properties are assumed to vary smoothly and continuously throughout three directions of the plate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this paper is to analyze and predict the central deflection, natural frequency, and critical buckling load of the multi-directional functionally graded (FG) plate with variable thickness resting on an elastic Winkler foundation. First, the mathematical models of the static and eigenproblems are formulated in great detail. The FG material properties are assumed to vary smoothly and continuously throughout three directions of the plate according to a Mori-Tanaka micromechanics model distribution of volume fraction of constituents. Then, finite element analysis (FEA) with mixed interpolation of tensorial components of 4-nodes (MITC4) is implemented in order to eliminate theoretically a shear locking phenomenon existing. Next, influences of the variable thickness functions (uniform, non-uniform linear, and non-uniform non-linear), material properties, length-to-thickness ratio, boundary conditions, and elastic parameters on the plate response are investigated and discussed in detail through several numerical examples. Finally, a deep neural network (DNN) technique using batch normalization (BN) is learned to predict the non-dimensional values of multi-directional FG plates. The DNN model also shows that it is a powerful technique capable of handling an extensive database and different vital parameters in engineering applications.

Related papers

• Variational manifolds for ground states and scarred dynamics of blockade-constrained spin models on two and three dimensional lattices [0.0]
  We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-1/2 systems. Our method can be interpreted as a generalization of mean-field theory to constrained spin models.
  arXiv  Detail & Related papers  (2023-11-15T13:52:21Z)
• Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs [1.1762713843017176]
  CMAS, a mixture of calcia, magnesia, alumina, and silicate, is characterized by its high viscosity, density, and surface tension. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the dynamics of highly molten CMAS droplets.
  arXiv  Detail & Related papers  (2023-07-18T10:52:08Z)
• Tunable non-additivity in Casimir-Lifshitz force between graphene gratings [7.633060349568631]
  We investigate the Casimir-Lifshitz force (CLF) between two identical graphene strip gratings. We take into account the high-order electromagnetic diffractions, the multiple scattering and the exact 2D feature of the graphene strips. We show that the non-additivity, which is one of the most interesting features of the CLF in general, is significantly high and can be modulated in situ.
  arXiv  Detail & Related papers  (2023-06-30T13:28:28Z)
• Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem [141.5628276096321]
  We present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem.
  arXiv  Detail & Related papers  (2023-06-28T11:15:53Z)
• StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks [1.9260081982051918]
  We introduce a novel data-driven approach to efficiently predict the elastic properties of crystal structures. This approach yields important scalar elastic moduli with the accuracy comparable to recent data-driven studies.
  arXiv  Detail & Related papers  (2023-06-22T11:34:08Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Deep equilibrium models as estimators for continuous latent variables [10.244213671349225]
  We show explicit relationships between neural network architectures and statistical models. We find that deep equilibrium models solve maximum a-posteriori (MAP) estimates for the latents and parameters of the transformation. Our DEQ feature maps are end-to-end differentiable, enabling fine-tuning for downstream tasks.
  arXiv  Detail & Related papers  (2022-11-11T01:21:34Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling [76.89779231460193]
  factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. We propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM) for sparse predictive analytics.
  arXiv  Detail & Related papers  (2021-04-05T00:02:36Z)
• Feature Weighted Non-negative Matrix Factorization [92.45013716097753]
  We propose the Feature weighted Non-negative Matrix Factorization (FNMF) in this paper. FNMF learns the weights of features adaptively according to their importances. It can be solved efficiently with the suggested optimization algorithm.
  arXiv  Detail & Related papers  (2021-03-24T21:17:17Z)
• Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
  empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
  arXiv  Detail & Related papers  (2020-06-11T09:58:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.