Pages that link to "Item:Q2083124"
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The following pages link to CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124):
Displaying 12 items.
- SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
- Phase-field DeepONet: physics-informed deep operator neural network for fast simulations of pattern formation governed by gradient flows of free-energy functionals (Q6084433) (← links)
- BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
- Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
- CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q6379311) (← links)
- Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
- PINN enhanced extended multiscale finite element method for fast mechanical analysis of heterogeneous materials (Q6576389) (← links)
- A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics (Q6588318) (← links)
- Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients (Q6590262) (← links)
- Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
- Finite element-integrated neural network framework for elastic and elastoplastic solids (Q6663275) (← links)
- Kolmogorov-Arnold-informed neural network: a physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov-Arnold networks (Q6669014) (← links)
