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Decoupling Stiffness and Peak Moment Via Hierarchical Snapping Structures Designed with Machine Learning

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Data-driven computational mechanics
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Computational mechanics enhanced by deep learning
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Data-driven approach for dynamic homogenization using meta learning
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Phase-change materials to improve solar panel's performance
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Mechanics of curved-ligament hexachiral metastructures under planar deformations
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Folding and deployment of curved tape springs
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Combination of Lightweight Elements and Nanostructured Materials for Batteries
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Harnessing instabilities for design of soft reconfigurable auxetic/chiral materials
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Deployment dynamics of tape springs
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Buckling-Induced Kirigami
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Neural Networks and Deep Learning
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Machine learning methods for fully automatic recognition of facial expressions and facial actions
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Three-Dimensional-Printed Multistable Mechanical Metamaterials With a Deterministic Deformation Sequence
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Designing complex architectured materials with generative adversarial networks
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Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems
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Modeling and visualization of leaf venation patterns
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Multidiscipline Topology Optimization of Stiffened Plate/Shell Structures Inspired by Growth Mechanisms of Leaf Veins in Nature
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Plant Systematics: A Phylogenetic Approach
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1605.08695openalex-extracted
TensorFlow: A system for large-scale machine learning
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An overview of gradient descent optimization algorithms
inferredcites →https://doi.org/10.48550/arxiv.1805.11604openalex-extracted
How Does Batch Normalization Help Optimization?
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TensorFlow: A system for large-scale machine learning
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