Deep generative modeling for mechanistic-based learning and design of metamaterial systems

DOI10.1016/j.cma.2020.113377zbMath1506.74216arXiv2006.15274OpenAlexW3083791730MaRDI QIDQ2020976

Yanyan Li

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.15274

zbMATH Keywords

functionally graded materialmetamaterialboundary connectivitymultiscale designdata-driven designdeep generative model

Mathematics Subject Classification ID

Control, switches and devices (``smart materials) in solid mechanics (74M05) Contact in solid mechanics (74M15)

Related Items (15)

What Machine Learning Can Do for Computational Solid Mechanics ⋮ Generalized de-homogenization via sawtooth-function-based mapping and its demonstration on data-driven frequency response optimization ⋮ Learning finite element convergence with the multi-fidelity graph neural network ⋮ IH-GAN: a conditional generative model for implicit surface-based inverse design of cellular structures ⋮ A deep learning energy method for hyperelasticity and viscoelasticity ⋮ VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems ⋮ Controlling auxeticity in curved-beam metamaterials via a deep generative model ⋮ DL-MSTO+: a deep learning-based multi-scale topology optimization framework via positive definiteness ensured material representation network ⋮ Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties ⋮ Intelligent optimum design of large-scale gradual-stiffness stiffened panels via multi-level dimension reduction ⋮ A complete physics-informed neural network-based framework for structural topology optimization ⋮ AI in computational mechanics and engineering sciences ⋮ De-homogenization using convolutional neural networks ⋮ Data-driven multifidelity topology design using a deep generative model: application to forced convection heat transfer problems ⋮ Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains

Cites Work

• A note on two problems in connexion with graphs
• Multi-scale concurrent material and structural design under mechanical and thermal loads
• Multi-objective concurrent topology optimization of thermoelastic structures composed of homogeneous porous material
• Multiphase composites with extremal bulk modulus
• Concurrent topology optimization design of material and structure within \(\operatorname{FE}^2\) nonlinear multiscale analysis framework
• Multiscale structural topology optimization with an approximate constitutive model for local material microstructure
• Functionally graded lattice structure topology optimization for the design of additive manufactured components with stress constraints
• Topology optimization for multiscale design of porous composites with multi-domain microstructures
• Multiscale topology optimization using neural network surrogate models
• An efficient concurrent topology optimization approach for frequency response problems
• Designing phononic crystal with anticipated band gap through a deep learning based data-driven method
• Topology optimization for concurrent design of layer-wise graded lattice materials and structures
• Elastic textures for additive fabrication
• The method of moving asymptotes—a new method for structural optimization
• Unnamed Item
• Unnamed Item

This page was built for publication: Deep generative modeling for mechanistic-based learning and design of metamaterial systems