Pages that link to "Item:Q2309802"
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The following pages link to Data driven modeling of plastic deformation (Q2309802):
Displaying 21 items.
- Predicting flow strength of austenitic steels with an IPANN model using different training strategies (Q1852163) (← links)
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
- Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning (Q1986877) (← links)
- Data science for finite strain mechanical science of ductile materials (Q1999552) (← links)
- Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
- Deep learning and crystal plasticity: a preconditioning approach for accurate orientation evolution prediction (Q2072494) (← links)
- A machine learning based plasticity model using proper orthogonal decomposition (Q2184326) (← links)
- Material behavior modeling with multi-output support vector regression (Q2282402) (← links)
- A computationally efficient ductile damage model accounting for nucleation and micro-inertia at high triaxialities (Q2310893) (← links)
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (Q2310917) (← links)
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites (Q2319390) (← links)
- Machine learning constitutive models of elastomeric foams (Q2670325) (← links)
- Data-driven elasto-(visco)-plasticity involving hidden state variables (Q2679308) (← links)
- Enhancing phenomenological yield functions with data: challenges and opportunities (Q2692822) (← links)
- On a Physics-Compatible Approach for Data-Driven Computational Mechanics (Q5051039) (← links)
- Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations (Q6051370) (← links)
- Distance minimizing based <scp>data‐driven</scp> computational method for the finite deformation of hyperelastic materials (Q6062837) (← links)
- Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints (Q6070057) (← links)
- Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
- Physics-informed machine-learning model of temperature evolution under solid phase processes (Q6159327) (← links)
- Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
