Published August 10, 2023 | Version Metamaterials
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ShengzhiLuan/2D-Cellular-Metamaterials: 2D Cellular Metamaterials

Authors/Creators

  • 1. Johns Hopkins University

Description

This repository provides data and associated codes for a data-driven framework that enables prediction of macroscopic properties of 2D cellular metamaterials, and identifies their connection to key morphological characteristics, as identified by the integration of machine-learning models (Random Forest and GAM) and interpretability algorithms (SHAP analysis).

Files

ShengzhiLuan/2D-Cellular-Metamaterials-Metamaterials.zip

Files (45.3 MB)

Additional details