  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.
