Pages that link to "Item:Q2246296"
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The following pages link to Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296):
Displaying 36 items.
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- A novel surrogate-model based active learning method for structural reliability analysis (Q2136699) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- SEM: a shallow energy method for finite deformation hyperelasticity problems (Q2141514) (← links)
- A novel localized collocation solver based on a radial Trefftz basis for thermal conduction analysis in FGMs with exponential variations (Q2147296) (← links)
- Solving flows of dynamical systems by deep neural networks and a novel deep learning algorithm (Q2168118) (← links)
- A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
- A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
- Stochastic projection based approach for gradient free physics informed learning (Q2686876) (← links)
- Deep physics corrector: a physics enhanced deep learning architecture for solving stochastic differential equations (Q2687567) (← links)
- Nonlinear dynamic stability analysis of axial impact loaded structures via the nonlocal strain gradient theory (Q2692073) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
- On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
- A data‐driven bond‐based peridynamic model derived from group method of data handling neural network with genetic algorithm (Q6092283) (← links)
- BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← 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)
- Brain MRI Images Classifications with Deep Fuzzy Clustering and Deep Residual Network (Q6172991) (← links)
- Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media (Q6172992) (← links)
- Predicting the Pore-Pressure and Temperature of Fire-Loaded Concrete by a Hybrid Neural Network (Q6172999) (← links)
- Engineered Interphase Mechanics in Single Lap Joints: Analytical and PINN Formulations (Q6173007) (← links)
- The effect of auxeticity on the vibration of conical sandwich shells with ring support under various boundary conditions (Q6539052) (← links)
- Investigating deep energy method applications in thermoelasticity (Q6545726) (← links)
- N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
- A cell-based smoothed finite element method for finite elasticity (Q6573169) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Energy-informed graph transformer model for solid mechanical analyses (Q6591778) (← links)
- Predictions of transient vector solution fields with sequential deep operator network (Q6597685) (← links)
- Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
- Finite element analysis with deformed shape constraints generated by laser-scanned point clouds (Q6648544) (← links)
- Simple yet effective adaptive activation functions for physics-informed neural networks (Q6660250) (← 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)
