This technology uses material geometry and microstructure to generate strain and stress fields via a deep learning model. The method includes using geometric images as inputs into a generator to produce fake stress or strain field images of composites under mechanical tests. The fake field images are then analyzed in a discriminator neural network to determine if the fake field images represent real stress and strain images produced by multiscale modeling methods (i.e., finite element analysis). This step is repeated until the discriminator can optimize its ability to identify the fake field images produced by the generator. The deep learning model is trained once the discriminator and generator reach Nash equilibrium.
Once the deep learning model is trained, it can be used to predict new field images and bypass conventional numerical modeling.