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Inverse Design of Two-Dimensional Nonlinear Mechanical Metamaterials Via Deep Learning

https://doi.org/10.2139/ssrn.5400971
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Accelerating Auxetic Metamaterial Design with Deep Learning
inferredcites →https://doi.org/10.1002/adma.202303481openalex-snapshot
Deep Learning for Size‐Agnostic Inverse Design of Random‐Network 3D Printed Mechanical Metamaterials
inferredcites →https://doi.org/10.1002/advs.202204977openalex-snapshot
Inverse Design of Energy‐Absorbing Metamaterials by Topology Optimization
inferredcites →https://doi.org/10.1007/978-981-15-6446-8openalex-snapshot
Mechanics of Metamaterials with Negative Parameters
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Auxetic Materials and Structures
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Data-driven design exploration method using conditional variational autoencoder for airfoil design
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A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images
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Additive manufacturing of medical instruments: A state-of-the-art review
inferredcites →https://doi.org/10.1016/j.apmt.2019.100456openalex-snapshot
Topology design of 2D and 3D elastic material microarchitectures with crystal symmetries displaying isotropic properties close to their theoretical limits
inferredcites →https://doi.org/10.1016/j.compstruct.2021.113857openalex-snapshot
Novel beam-like mechanical metamaterials with different flexural rigidities in two directions
inferredcites →https://doi.org/10.1016/j.engappai.2021.104560openalex-snapshot
Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders
inferredcites →https://doi.org/10.1016/j.jnoncrysol.2009.05.032openalex-snapshot
Finite element analysis of auxetic obstacle deformation and fluid flow in a channel
inferredcites →https://doi.org/10.1016/j.matdes.2020.108970openalex-snapshot
Deep neural network approach to estimate biaxial stress-strain curves of sheet metals
inferredcites →https://doi.org/10.1016/j.matdes.2021.110178openalex-snapshot
Controllable inverse design of auxetic metamaterials using deep learning
inferredcites →https://doi.org/10.1016/j.mechmat.2020.103625openalex-snapshot
Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship
inferredcites →https://doi.org/10.1016/j.mechmat.2021.104191openalex-snapshot
Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks
inferredcites →https://doi.org/10.1016/j.mser.2021.100606openalex-snapshot
Architected cellular materials: A review on their mechanical properties towards fatigue-tolerant design and fabrication
inferredcites →https://doi.org/10.1016/j.mtphys.2022.100616openalex-snapshot
Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning
inferredcites →https://doi.org/10.1038/s42005-019-0251-4openalex-snapshot
Harnessing energy landscape exploration to control the buckling of cylindrical shells
inferredcites →https://doi.org/10.1039/c0cs00184hopenalex-snapshot
Metamaterials: a new frontier of science and technology
inferredcites →https://doi.org/10.1080/09506608.2020.1815394openalex-snapshot
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
inferredcites →https://doi.org/10.1088/1361-665x/aba53copenalex-snapshot
Lightweight mechanical metamaterials designed using hierarchical truss elements
inferredcites →https://doi.org/10.1109/tnnls.2021.3084827openalex-snapshot
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
inferredcites →https://doi.org/10.1126/science.abn1459openalex-snapshot
Growth rules for irregular architected materials with programmable properties
inferredcites →https://doi.org/10.1146/annurev-matsci-070115-031624openalex-snapshot
Architected Cellular Materials
inferredcites →https://doi.org/10.1557/mrc.2015.51openalex-snapshot
Vibrant times for mechanical metamaterials
inferredcites →https://doi.org/10.2320/matertrans.p-m2020853openalex-snapshot
Estimation of Texture-Dependent Stress-Strain Curve and r-Value of Aluminum Alloy Sheet Using Deep Learning
inferredcites →https://doi.org/10.3390/ma10121386openalex-snapshot
Computational Modelling of Structures with Non-Intuitive Behaviour
inferredcites →https://doi.org/10.48550/arxiv.1411.1784openalex-snapshot
Conditional Generative Adversarial Nets
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