Pages that link to "Item:Q2020976"
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The following pages link to Deep generative modeling for mechanistic-based learning and design of metamaterial systems (Q2020976):
Displaying 23 items.
- De-homogenization using convolutional neural networks (Q2060089) (← links)
- Data-driven multifidelity topology design using a deep generative model: application to forced convection heat transfer problems (Q2060177) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm (Q2096817) (← links)
- Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
- Inverse design of locally resonant metabarrier by deep learning with a rule-based topology dataset (Q2136755) (← links)
- Generalized de-homogenization via sawtooth-function-based mapping and its demonstration on data-driven frequency response optimization (Q2142159) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- IH-GAN: a conditional generative model for implicit surface-based inverse design of cellular structures (Q2156772) (← links)
- Rapid design of metamaterials via multitarget Bayesian optimization (Q2245155) (← links)
- A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
- Optimised graded metamaterials for mechanical energy confinement and amplification via reinforcement learning (Q2692865) (← links)
- AI in computational mechanics and engineering sciences (Q2693415) (← links)
- What Machine Learning Can Do for Computational Solid Mechanics (Q5051038) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- Controlling auxeticity in curved-beam metamaterials via a deep generative model (Q6094692) (← links)
- DL-MSTO+: a deep learning-based multi-scale topology optimization framework via positive definiteness ensured material representation network (Q6096498) (← links)
- Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties (Q6099231) (← links)
- Intelligent optimum design of large-scale gradual-stiffness stiffened panels via multi-level dimension reduction (Q6121702) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- A complete physics-informed neural network-based framework for structural topology optimization (Q6194165) (← links)
- Machine learning in solid mechanics: application to acoustic metamaterial design (Q6592358) (← links)
- DiffMat: data-driven inverse design of energy-absorbing metamaterials using diffusion model (Q6643604) (← links)
