On the potential of recurrent neural networks for modeling path dependent plasticity

Keyphrases

• Path Dependence 100%
• Stress-strain Response 100%
• Neural Network Model 100%
• Recurrent Neural Network 100%
• Axial Stress 100%
• Loading Path 100%
• Yld2000-2d 100%
• Homogeneous Anisotropic Hardening 100%
• Neural Network 50%
• Physics-based 50%
• Nonlinearity 50%
• Increasing Complexity 50%
• Modeling Approach 50%
• Machine Learning Models 50%
• Data-driven Modeling 50%
• Mathematical Description 50%
• Tensorial 50%
• 2D Model 50%
• Evolution Equations 50%
• Recurrent Neural Network Model 50%
• Stress Plasticity 50%
• Unit Cell Model 50%
• Plane Stress 50%
• Arbitrary Loading 50%
• Modelling Problems 50%
• Plasticity Model 50%
• Elastoplasticity 50%
• Bauschinger Effect 50%
• Elastoplastic Behavior 50%
• Permanent Softening 50%
• Two-dimensional Foam 50%
• Latent Hardening 50%
• Fully Convolutional Neural Network 50%
• Fully Connected Layer 50%
• Gated Recurrent Unit 50%

Engineering

• Anisotropic 100%
• Network Model 100%
• Recurrent Neural Network 100%
• Loading Path 66%
• Stress-Strain Relations 66%
• Plane Stress 33%
• Two Dimensional 33%
• Axial Stress 33%
• Recurrent 33%
• Plasticity Model 33%
• Internal Variable 33%
• Mathematical Description 33%
• Hardening Model 33%
• Permanent Softening 33%
• Bauschinger Effect 33%
• Learning System 33%
• Nonlinearity 33%