A hybrid deep learning approach for the design of 2D low porosity auxetic metamaterials

Abstract

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has been proven to be a powerful tool in many tasks related to the design of metamaterials. DL-aided design techniques can be generally categorized into two types, including forward designs, which are training surrogate models to accelerate the simulation process, and inverse design, which uses inverse modeling techniques to generate the design that satisfies the corresponding requirement. Although generative models have a unique capability to generate multiple designs instantly with random information, they often underperform in accuracy compared to designs based on optimization techniques. In this paper, a hybrid design framework combining the advantages of both DL forward design and the inverse design based on the mixture density network (MDN) is proposed. Then the proposed framework is implemented for the inverse design of S-shaped perforated auxetic metamaterial. The hybrid design framework inherited the one-to-many mapping capability of MDN and has great capability of generating designs with designated mechanical properties at less than 10% relative errors, in most design scenarios (over 95% in the test set), at one to two orders of magnitude less computational cost compared to optimization-based forward design.