Pages that link to "Item:Q2237330"
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The following pages link to A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330):
Displaying 28 items.
- Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- Physics-informed neural networks for shell structures (Q2102673) (← links)
- A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures (Q2109538) (← links)
- SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Coercing machine learning to output physically accurate results (Q2223280) (← links)
- Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Optimum design of nonlinear structures via deep neural network-based parameterization framework (Q2691035) (← links)
- A peridynamic-informed neural network for continuum elastic displacement characterization (Q2693390) (← links)
- A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← 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)
- Deep Ritz method with adaptive quadrature for linear elasticity (Q6096475) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- Physically informed deep homogenization neural network for unidirectional multiphase/multi-inclusion thermoconductive composites (Q6101900) (← links)
- Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (Q6121800) (← links)
- Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy (Q6153887) (← links)
- A complete physics-informed neural network-based framework for structural topology optimization (Q6194165) (← links)
- A nonlocal energy-informed neural network for peridynamic correspondence material models (Q6545792) (← links)
- Physics-informed convolutional transformer for predicting volatility surface (Q6546314) (← links)
- Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
- An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385) (← links)
- Physics-informed discretization-independent deep compositional operator network (Q6609787) (← links)
