Pages that link to "Item:Q2671703"
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The following pages link to A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703):
Displaying 28 items.
- Smart finite elements: a novel machine learning application (Q1986797) (← links)
- Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics (Q1989089) (← links)
- Deep learned finite elements (Q2021024) (← links)
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
- Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations (Q2160446) (← links)
- Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications (Q2310233) (← links)
- Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- Parametric stress field solutions for heterogeneous materials using proper generalized decomposition (Q2683330) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
- A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← links)
- Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach (Q6084532) (← links)
- On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
- Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads (Q6096499) (← links)
- Adaptive task decomposition physics-informed neural networks (Q6120149) (← links)
- Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (Q6121800) (← links)
- Geometrically-driven generation of mechanical designs through deep convolutional GANs (Q6495518) (← links)
- Investigating deep energy method applications in thermoelasticity (Q6545726) (← links)
- A neural network finite element approach for high speed cardiac mechanics simulations (Q6557830) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Efficient damage prediction and sensitivity analysis in rectangular welded plates subjected to repeated blast loads utilizing deep learning networks (Q6661857) (← links)
