Pages that link to "Item:Q2679297"

The following pages link to Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297):

Displaying 21 items.

• Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
• Model-free data-driven identification algorithm enhanced by local manifold learning (Q2692900) (← links)
• A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads (Q6082574) (← 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)
• A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content (Q6096512) (← links)
• \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
• Incremental neural controlled differential equations for modeling of path-dependent material behavior (Q6125468) (← links)
• Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
• Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
• A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture (Q6185157) (← links)
• Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials (Q6397962) (← links)
• Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified constitutive relation error framework (Q6497210) (← links)
• Recurrent neural network plasticity models: unveiling their common core through multi-task learning (Q6550155) (← links)
• A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
• A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)
• N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
• Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials (Q6595896) (← links)
• A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials (Q6595912) (← links)
• Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
• Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables (Q6661941) (← links)