A physics-informed GAN framework based on model-free data-driven computational mechanics

Model -free data -driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample datasets 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.