Pages that link to "Item:Q2274318"
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The following pages link to Neural networks for topology optimization (Q2274318):
Displaying 31 items.
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (Q2020738) (← links)
- Universal machine learning for topology optimization (Q2022034) (← links)
- De-homogenization using convolutional neural networks (Q2060089) (← links)
- IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← links)
- Deep reliability learning with latent adaptation for design optimization under uncertainty (Q2145132) (← links)
- Machine learning for topology optimization: physics-based learning through an independent training strategy (Q2160385) (← links)
- Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design (Q2205158) (← links)
- Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy (Q2237305) (← links)
- TONR: an exploration for a novel way combining neural network with topology optimization (Q2246269) (← links)
- Machine learning-combined topology optimization for functionary graded composite structure design (Q2246382) (← links)
- Accurate and real-time structural topology prediction driven by deep learning under moving morphable component-based framework (Q2247294) (← links)
- FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
- Isogeometric topology optimization based on deep learning (Q2674045) (← links)
- Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- Cross-resolution acceleration design for structural topology optimization based on deep learning (Q3385176) (← links)
- FIR-type and IIR-type neural networks, and their application to shape optimization of a magnetic pole (Q3588265) (← links)
- Deep learning driven real time topology optimization based on improved convolutional block attention (Cba-U-Net) model (Q6042422) (← links)
- A deep convolutional neural network for topology optimization with perceptible generalization ability (Q6048164) (← links)
- Geometric optimization of vascular stents modeled as networks of 1D rods (Q6087913) (← links)
- Topology optimization via implicit neural representations (Q6097597) (← links)
- Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization (Q6164267) (← links)
- A complete physics-informed neural network-based framework for structural topology optimization (Q6194165) (← links)
- Geometrically-driven generation of mechanical designs through deep convolutional GANs (Q6495518) (← links)
- TO-NODE: topology optimization with neural ordinary differential equation (Q6499906) (← links)
- Comparative study on manifold learning approaches for parametric topology optimization problem via unsupervised deep learning (Q6547730) (← links)
- Topologically optimal design and failure prediction using conditional generative adversarial networks (Q6554040) (← links)
- Real-time topology optimization via learnable mappings (Q6589328) (← links)
- Predictions of transient vector solution fields with sequential deep operator network (Q6597685) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Structural topology optimization based on deep learning (Q6648428) (← links)
