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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
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Novel 3D‐Printed Hybrid Auxetic Mechanical Metamaterial with Chirality‐Induced Sequential Cell Opening Mechanisms
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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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Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations
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Exploiting negative Poisson's ratio to design 3D-printed composites with enhanced mechanical properties
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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
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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
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Exploiting the Dynamics of Soft Materials for Machine Learning
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Mechanical Design of Antichiral-Reentrant Hybrid Intravascular Stent
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Review of Mechanics and Applications of Auxetic Structures
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Influence of Negative Poisson′s Ratio on Stent Applications
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Effect of poisson's ratio on functionally graded cellular structures
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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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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Secondary Modeling Method for Constructing 3d Negative Poisson's Ratio Materials with High Specific Strength Based on Common Lattice Structures
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Auxetic Metamaterials for Biomedical Devices: Current Situation, Main Challenges, and Research Trends
inferred← citeshttps://doi.org/10.3390/mi12111313openalex-snapshot
A Novel High-Speed and High-Accuracy Mathematical Modeling Method of Complex MEMS Resonator Structures Based on the Multilayer Perceptron Neural Network
inferred← citeshttps://doi.org/10.3788/col202624.042201openalex-snapshot
Deep-learning-based multilayer nano-film structure reverse design for efficient passive radiative cooling
inferred← citeshttps://doi.org/10.3929/ethz-b-000643223openalex-snapshot
Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
inferred← citeshttps://doi.org/10.70675/807ea578zd70dz4898zb642zb1cdc00dcd86openalex-snapshot
Improving Scalability and Inference in Probabilistic Deep Models
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