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Accelerating Auxetic Metamaterial Design with Deep Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Soft Actuation of Structured Cylinders through Auxetic Behavior
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Three Decades of Auxetics Research − Materials with Negative Poisson's Ratio: A Review
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3D Printed Chiral Cellular Solids with Amplified Auxetic Effects Due to Elevated Internal Rotation
inferredcites →https://doi.org/10.1002/adem.201700744openalex-snapshot
Novel 3D‐Printed Hybrid Auxetic Mechanical Metamaterial with Chirality‐Induced Sequential Cell Opening Mechanisms
inferredcites →https://doi.org/10.1002/adma.201004090openalex-snapshot
Design of Auxetic Structures via Mathematical Optimization
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Hierarchically Enhanced Impact Resistance of Bioinspired Composites
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Effect of Constituent Materials on Composite Performance: Exploring Design Strategies via Machine Learning
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Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities
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Analogies across auxetic models based on deformation mechanism
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Comparison results for eigenvalues of curl curl operator and Stokes operator
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Seeing auxetic materials from the mechanics point of view: A structural review on the negative Poisson’s ratio
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De novo composite design based on machine learning algorithm
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Architected metamaterials with tailored 3D buckling mechanisms at the microscale
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Printing nature: Unraveling the role of nacre's mineral bridges
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Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations
inferredcites →https://doi.org/10.1016/j.matdes.2018.01.034openalex-snapshot
Exploiting negative Poisson's ratio to design 3D-printed composites with enhanced mechanical properties
inferredcites →https://doi.org/10.1016/j.mechmachtheory.2018.08.010openalex-snapshot
Design and modeling of a soft robotic surface with hyperelastic material
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Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning
inferredcites →https://doi.org/10.1021/acsnano.8b03569openalex-snapshot
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
inferredcites →https://doi.org/10.1038/nature14543openalex-snapshot
Design, fabrication and control of soft robots
inferredcites →https://doi.org/10.1038/ncomms7566openalex-snapshot
Soft network composite materials with deterministic and bio-inspired designs
inferredcites →https://doi.org/10.1038/s41524-018-0094-7openalex-snapshot
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
inferredcites →https://doi.org/10.1038/s41598-018-26980-7openalex-snapshot
Multi-material Additive Manufacturing of Metamaterials with Giant, Tailorable Negative Poisson’s Ratios
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Stiff auxetics: Hierarchy as a route to stiff, strong lattice based auxetic meta-materials
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Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
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Ultra-programmable buckling-driven soft cellular mechanisms
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Multi-material 3D printed mechanical metamaterials: Rational design of elastic properties through spatial distribution of hard and soft phases
inferredcites →https://doi.org/10.1088/1361-665x/aaa3cfopenalex-snapshot
Behavior of auxetic structures under compression and impact forces
inferredcites →https://doi.org/10.1088/1361-665x/aaa61copenalex-snapshot
Auxetic metamaterials and structures: a review
inferredcites →https://doi.org/10.1088/1402-4896/aab4e2openalex-snapshot
Materials-by-design: computation, synthesis, and characterization from atoms to structures
inferredcites →https://doi.org/10.1089/3dp.2017.0027openalex-snapshot
Large Shape Transforming 4D Auxetic Structures
inferredcites →https://doi.org/10.1089/soro.2017.0075openalex-snapshot
Exploiting the Dynamics of Soft Materials for Machine Learning
inferredcites →https://doi.org/10.1142/s1758825118501053openalex-snapshot
Mechanical Design of Antichiral-Reentrant Hybrid Intravascular Stent
inferredcites →https://doi.org/10.1155/2014/753496openalex-snapshot
Review of Mechanics and Applications of Auxetic Structures
inferredcites →https://doi.org/10.11648/j.am.20130203.14openalex-snapshot
Influence of Negative Poisson′s Ratio on Stent Applications
inferredcites →https://doi.org/10.1166/mex.2016.1341openalex-snapshot
Effect of poisson's ratio on functionally graded cellular structures
inferredcites →https://doi.org/10.1557/mrc.2019.32openalex-snapshot
Machine learning for composite materials
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Using convolutional neural networks to predict composite properties beyond the elastic limit
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Printing mesoscale architectures
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Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges
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Scikit-learn: Machine Learning in Python
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How machine learning can help the design and analysis of composite materials and structures?
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