Pages that link to "Item:Q2021893"

The following pages link to A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893):

Displaying 50 items.

• A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
• A neural network-based approach for bending analysis of strain gradient nanoplates (Q6044720) (← links)
• Machine Learning Surrogate Modeling for Meshless Methods: Leveraging Universal Approximation (Q6048309) (← links)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• One-dimensional ice shelf hardness inversion: clustering behavior and collocation resampling in physics-informed neural networks (Q6054214) (← links)
• Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
• Deep learning‐based reduced order models for the real‐time simulation of the nonlinear dynamics of microstructures (Q6071430) (← links)
• Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
• A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads (Q6082574) (← links)
• A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← links)
• StressD: 2D stress estimation using denoising diffusion model (Q6084486) (← links)
• On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← 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)
• Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
• Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
• A class of mesh-free algorithms for some problems arising in finance and machine learning (Q6101663) (← links)
• Learning Markovian Homogenized Models in Viscoelasticity (Q6109142) (← links)
• Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
• Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance (Q6116144) (← links)
• HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
• Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307) (← links)
• Adaptive task decomposition physics-informed neural networks (Q6120149) (← links)
• Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (Q6121800) (← links)
• Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
• SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain (Q6137634) (← links)
• Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
• Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification (Q6164278) (← links)
• Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions (Q6171154) (← links)
• Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
• PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation (Q6171227) (← links)
• Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics (Q6171233) (← links)
• Distributed PINN for Linear Elasticity — A Unified Approach for Smooth, Singular, Compressible and Incompressible Media (Q6172992) (← links)
• Adaptive transfer learning for PINN (Q6173323) (← links)
• Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks (Q6173359) (← links)
• A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture (Q6185157) (← links)
• AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems (Q6194971) (← links)
• Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data (Q6201157) (← links)
• A surrogate model based on deep convolutional neural networks for solving deformation caused by moisture diffusion (Q6540139) (← links)
• A nonlocal energy-informed neural network for peridynamic correspondence material models (Q6545792) (← links)
• The coupled physical-informed neural networks for the two phase magnetohydrodynamic flows (Q6549888) (← links)
• Recurrent neural network plasticity models: unveiling their common core through multi-task learning (Q6550155) (← links)
• Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
• Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
• Physics-informed machine learning in asymptotic homogenization of elliptic equations (Q6557811) (← links)
• Gradient enhanced physics-informed neural network for iterative form-finding of tensile membrane structures by potential energy minimization (Q6558171) (← links)
• A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)