Pages that link to "Item:Q2309378"
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The following pages link to FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378):
Displaying 22 items.
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
- Deep learning and crystal plasticity: a preconditioning approach for accurate orientation evolution prediction (Q2072494) (← links)
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
- Towards out of distribution generalization for problems in mechanics (Q2083180) (← links)
- DeLISA: deep learning based iteration scheme approximation for solving PDEs (Q2134800) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning (Q2145129) (← links)
- A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness (Q2237726) (← links)
- TONR: an exploration for a novel way combining neural network with topology optimization (Q2246269) (← links)
- Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites (Q2670357) (← links)
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
- MFLP-PINN: a physics-informed neural network for multiaxial fatigue life prediction (Q2691055) (← links)
- Learning topology optimization process via convolutional long‐short‐term memory autoencoder‐decoder (Q6062851) (← links)
- On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
- Locally assembled stiffness matrix: a novel method to obtain global stiffness matrix (Q6098676) (← links)
- Frankenstein's data-driven computing approach to model-free mechanics (Q6159315) (← links)
- HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis (Q6159332) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling (Q6185242) (← links)
- A complete physics-informed neural network-based framework for structural topology optimization (Q6194165) (← links)
- Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
