arXiv (Cornell University)
Inverse design of anisotropic microstructures using physics-augmented neural networks
December 2024 • Asghar Jadoon, Karl A. Kalina, Manuel K. Rausch, Reese Jones, Jan N. Fuhg
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-fold problem. First, we must learn the type and orientation of anisotropy and then find the optimal design parameters to achieve the desired mechanical response. In our work, we solve this challenge by first training a forward surrogate model based on the macroscopic stress-strain data obtained via computational homogenization for a give…