Inverse design of adaptive flexible structures using physical-enhanced neural network.

ABSTRACT: Traditional design and analysis of mechanical metamaterials are complex and time-consuming, owing to their nonlinear characteristics. This paper proposes a computationally efficient inverse design framework to predict the nonlinear strain-stress response considering the buckling behaviour under a tensile load. Design and simulation processes of the structures are based on the reduced order model (ROM) of flexible structures, all within a single software environment, MATLAB/Simscape, using the flexible beam blocks. The physical-enhanced neural network (PENN) design is implemented in MATLAB, utilising the results of the ROM model for training and testing. The ROM model takes 4.5 min on average on a 12-core CPU, whereas the trained PENN predicts the stiffness curve in a fraction of a second o

SUBMITTER: Mohammadi M 

PROVIDER: S-EPMC12502832 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature