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Found 7 papers, 4 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.
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
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.
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design.
Deep Generative Models for Geometric Design Under Uncertainty
Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization.
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy ($R^2$-scores between target properties and properties of generated unit cells $>98\%$) and 2) improve the optimized structural performance over the conventional variable-density single-type structure.
