Generative machine learning framework for inverse design of matamaterials based on prescribed mechanical behavior

This repository contains a source code and dataset used to generate results that are reported in the paper entitled of "Rapid Inverse Design of Metamaterials based on Prescribed Mechanical Behavior through Machine Learning". A generative machine learning framework utilizes an inverse prediction and forward validation modules where each module is composed of five distinct neural network models. The framework offers a rapid inverse design of metamaterials in response to user-defined uniaxial compressive mechanical behavior. The framework can be readily extended to inverse-design other mechanical behaviors (e.g., tension, shear, torsion, bending, etc.) when the input follows a similar form of the stress-strain curve used in the paper.