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Model-free data-driven computational mechanics, first proposed byKirchdoerfer and Ortiz, replace phenomenological models with numericalsimulations based on sample data sets in strain-stress space. In this study, weintegrate this paradigm within physics-informed generative adversarial networks(GANs). We enhance the conventional physics-informed neural network frameworkby implementing the principles of data-driven computational mechanics intoGANs. Specifically, the generator is informed by physical constraints, whilethe discriminator utilizes the closest strain-stress data to discern theauthenticity of the generator's output. This combined approach presents a newformalism to harness data-driven mechanics and deep learning to simulate andpredict mechanical behaviors.

A physics-informed GAN Framework based on Model-free Data-Driven Computational Mechanics