Pages that link to "Item:Q2180473"

The following pages link to MAP123-EP: a mechanistic-based data-driven approach for numerical elastoplastic analysis (Q2180473):

Displaying 14 items.

• Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (Q2020738) (← links)
• MAP123-EPF: a mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain (Q2020782) (← links)
• Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity (Q2083112) (← links)
• A data-driven approach for modeling tension-compression asymmetric material behavior: numerical simulation and experiment (Q2115577) (← links)
• A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features (Q2115607) (← links)
• Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← links)
• MAP123: a data-driven approach to use 1D data for 3D nonlinear elastic materials modeling (Q2179201) (← links)
• Data-driven elasto-(visco)-plasticity involving hidden state variables (Q2679308) (← links)
• Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
• An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials (Q6105206) (← links)
• Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics (Q6121553) (← links)
• Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
• Data-driven confidence bound for structural response using segmented least squares: a mixed-integer programming approach (Q6614962) (← links)
• A novel data-driven framework of elastoplastic constitutive model based on geometric physical information (Q6663333) (← links)