Model-free data-driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample data sets in strain-stress space. In this study, we integrate this paradigm within physics-informed generative adversarial networks (GANs). We enhance the conventional physics-informed neural network framework by implementing the principles of data-driven computational mechanics into GANs. Specifically, the generator is informed by physical constraints, while the discriminator utilizes the closest strain-stress data to discern the authenticity of the generator's output. This combined approach presents a new formalism to harness data-driven mechanics and deep learning to simulate and predict mechanical behaviors.
Cite
@article{arxiv.2310.20308,
title = {A physics-informed GAN Framework based on Model-free Data-Driven Computational Mechanics},
author = {Kerem Ciftci and Klaus Hackl},
journal= {arXiv preprint arXiv:2310.20308},
year = {2023}
}