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Found 6 papers, 2 papers with code
Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature.
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated.
Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending
We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i. e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility.
Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization
After integrating LVGP with the density-based TO, an efficient data-driven cellular composite optimization process is developed to enable concurrent exploration of microstructure concepts and the associated volume fractions for natural frequency optimization.
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems
For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space.
METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design
In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning.
