Pages that link to "Item:Q2021107"

The following pages link to Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107):

Displaying 50 items.

• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity (Q2083112) (← links)
• Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
• Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks (Q2115570) (← links)
• Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
• Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning (Q2145129) (← links)
• A multiscale, data-driven approach to identifying thermo-mechanically coupled laws -- bottom-up with artificial neural networks (Q2150265) (← links)
• Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
• Data-driven tissue mechanics with polyconvex neural ordinary differential equations (Q2160446) (← links)
• Model-free data-driven simulation of inelastic materials using structured data sets, tangent space information and transition rules (Q2171494) (← links)
• Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data (Q2246386) (← links)
• Machine learning constitutive models of elastomeric foams (Q2670325) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
• Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
• What Machine Learning Can Do for Computational Solid Mechanics (Q5051038) (← links)
• Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
• Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials (Q6061746) (← links)
• Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
• A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
• Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints (Q6070057) (← links)
• A comparative study on different neural network architectures to model inelasticity (Q6082629) (← links)
• A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures (Q6084451) (← links)
• On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
• Geometric learning for computational mechanics. III: Physics-constrained response surface of geometrically nonlinear shells (Q6096461) (← links)
• Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations (Q6097591) (← links)
• \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
• Incompressible rubber thermoelasticity: a neural network approach (Q6101617) (← links)
• Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria (Q6121688) (← links)
• Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
• Deep learning and multi-level featurization of graph representations of microstructural data (Q6159319) (← links)
• Nonlinear electro-elastic finite element analysis with neural network constitutive models (Q6497139) (← links)
• Peridynamic neural operators: a data-driven nonlocal constitutive model for complex material responses (Q6497150) (← links)
• I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← links)
• Meshless physics-informed deep learning method for three-dimensional solid mechanics (Q6554056) (← links)
• A microstructure-based graph neural network for accelerating multiscale simulations (Q6557762) (← links)
• N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
• Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations (Q6588310) (← links)
• Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
• A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials (Q6595912) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• Unsupervised machine learning classification for accelerating \(\mathrm{FE}^2\) multiscale fracture simulations (Q6641844) (← links)
• Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response (Q6641865) (← links)
• Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables (Q6661941) (← links)
• Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations (Q6669042) (← links)
• Machine-learning-based virtual fields method: application to anisotropic hyperelasticity (Q6669068) (← links)