Pages that link to "Item:Q2096848"

The following pages link to A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848):

Displaying 37 items.

• \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition (Q2021230) (← links)
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
• Non-invasive inference of thrombus material properties with physics-informed neural networks (Q2022055) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← 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)
• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• A peridynamic-informed neural network for continuum elastic displacement characterization (Q2693390) (← 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)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures (Q6058580) (← links)
• Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method (Q6095431) (← links)
• Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement (Q6096450) (← links)
• Discovering the mechanics of artificial and real meat (Q6096485) (← links)
• Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
• Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
• On automated model discovery and a universal material subroutine for hyperelastic materials (Q6118599) (← links)
• Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (Q6121800) (← links)
• Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
• Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
• Neuro-PINN: a hybrid framework for efficient nonlinear projection equation solutions (Q6569923) (← links)
• Phase-field modeling of fracture with physics-informed deep learning (Q6588261) (← links)
• Interface PINNs (I-PINNs): a physics-informed neural networks framework for interface problems (Q6588288) (← links)
• Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
• Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
• A transfer learning physics-informed deep learning framework for modeling multiple solute dynamics in unsaturated soils (Q6609790) (← links)
• Ensemble of physics-informed neural networks for solving plane elasticity problems with examples (Q6639905) (← links)
• Physics-informed holomorphic neural networks (PIHNNs): solving 2D linear elasticity problems (Q6643566) (← links)