Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
From MaRDI portal
Publication:2237774
DOI10.1016/j.cma.2021.114034zbMath1502.74109arXiv2209.04416OpenAlexW3184636400MaRDI QIDQ2237774
Qizhi He, Xiaolong He, Jiun-Shyan Chen
Publication date: 28 October 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2209.04416
deep learningbiological materialdata-driven computational mechanicsautoencodersconvexity-preserving reconstruction
Artificial neural networks and deep learning (68T07) Theory of constitutive functions in solid mechanics (74A20) Numerical and other methods in solid mechanics (74S99)
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Cites Work
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- A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation
- Artificial neural networks in numerical modelling of composites
- Data-driven problems in elasticity
- Finite element analysis of V-ribbed belts using neural network based hyperelastic material model
- Reproducing kernel particle methods for large deformation analysis of nonlinear structures
- A multilevel finite element method (FE\(^{2}\)) to describe the response of highly nonlinear structures using generalized continua.
- Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
- A manifold learning approach to data-driven computational elasticity and inelasticity
- Principal component analysis.
- A survey of parametrized variational principles and applications to computational mechanics
- An unsupervised data completion method for physically-based data-driven models
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Model-free data-driven inelasticity
- Model-free data-driven computational mechanics enhanced by tensor voting
- Variational framework for distance-minimizing method in data-driven computational mechanics
- A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
- \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
- Data driven computing with noisy material data sets
- A new reliability-based data-driven approach for noisy experimental data with physical constraints
- Data-based derivation of material response
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- Model-free data-driven methods in mechanics: material data identification and solvers
- Diffusion nets
- Data-driven computational mechanics
- What-you-prescribe-is-what-you-get orthotropic hyperelasticity
- Reducing the Dimensionality of Data with Neural Networks
- Learning Deep Architectures for AI
- A model of incompressible isotropic hyperelastic material behavior using spline interpolations of tension-compression test data
- THE PARTITION OF UNITY METHOD
- Numerical implementation of a neural network based material model in finite element analysis
- Non-linear version of stabilized conforming nodal integration for Galerkin mesh-free methods
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Reproducing kernel particle methods
- Nonlinear Dimensionality Reduction
