Pages that link to "Item:Q2160403"

The following pages link to Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403):

Displaying 42 items.

• Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids (Q785491) (← links)
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials (Q1986850) (← links)
• Smart constitutive laws: inelastic homogenization through machine learning (Q2020776) (← links)
• Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954) (← links)
• Scale bridging materials physics: active learning workflows and integrable deep neural networks for free energy function representations in alloys (Q2021083) (← links)
• 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)
• Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
• A multiscale, data-driven approach to identifying thermo-mechanically coupled laws -- bottom-up with artificial neural networks (Q2150265) (← links)
• A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths (Q2236174) (← links)
• Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks (Q2667309) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• A new family of constitutive artificial neural networks towards automated model discovery (Q2679491) (← links)
• Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
• Modeling of materials with fading memory using neural networks (Q3549786) (← links)
• Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
• Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
• A comparative study on different neural network architectures to model inelasticity (Q6082629) (← links)
• Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models (Q6089273) (← links)
• Automated model discovery for skin: discovering the best model, data, and experiment (Q6094670) (← links)
• Two-stage 2D-to-3d reconstruction of realistic microstructures: implementation and numerical validation by effective properties (Q6097657) (← links)
• \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
• Physically informed deep homogenization neural network for unidirectional multiphase/multi-inclusion thermoconductive composites (Q6101900) (← links)
• On the micromechanics of deep material networks (Q6115410) (← links)
• Incremental neural controlled differential equations for modeling of path-dependent material behavior (Q6125468) (← links)
• Neural integration for constitutive equations using small data (Q6153835) (← links)
• Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331) (← links)
• Elasticity-mechanics-informed generative adversarial networks for predicting the thermal strain of thermal barrier coatings penetrated by CaO-MgO-\(\mathrm{Al_2O}_3\)-\(\mathrm{SiO}_2\) (Q6163041) (← links)
• Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified constitutive relation error framework (Q6497210) (← links)
• NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks (Q6499924) (← links)
• Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
• Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
• tLaSDI: thermodynamics-informed latent space dynamics identification (Q6588297) (← 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)
• Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
• Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables (Q6661941) (← links)
• Finite element-integrated neural network framework for elastic and elastoplastic solids (Q6663275) (← links)
• A novel data-driven framework of elastoplastic constitutive model based on geometric physical information (Q6663333) (← links)
• Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations (Q6669042) (← links)