Pages that link to "Item:Q2459257"
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The following pages link to Characterizing rate-dependent material behaviors in self-learning simulation (Q2459257):
Displaying 11 items.
- Neural network constitutive model for crystal structures (Q2033624) (← links)
- Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
- A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (Q2179220) (← 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)
- Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770) (← links)
- Computational mechanics enhanced by deep learning (Q2310108) (← links)
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (Q2310917) (← links)
- Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections (Q2638009) (← links)
- Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data (Q6201157) (← links)
- Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion (Q6604138) (← links)
