Published August 3, 2023
| Version Version 1.0.0
Journal article
Open
Generative machine learning framework for inverse design of matamaterials based on prescribed mechanical behavior
Authors/Creators
- 1. Virginia Tech
- 2. University of California, Berkeley
- 3. Oklahoma University
- 4. Sichuan University
- 5. University of California, Los Angeles
Description
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.
Notes
The authors gratefully acknowledges the Office of Naval Research (Grant No. N00014-20-1-2504:P00001), National Science Foundation DMREF grant (Grant No. 2119643), and the Air Force Office of Scientific Research (Grant No. FA9550‐18‐1‐0299).
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