Abstract: Materials-by-design is a new paradigm to develop novel high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design spaces. The disclosure is a novel AI-based approach, implemented in a game-theory based generative adversarial neural network (GAN), to bridge the gap between the physical performance and design space. A end-to-end deep learning model predicts physical fields like stress or strain directly from the material geometry and microstructure. The model reaches an astonishing accuracy not only for predicted field data but also for secondary predictions, such as average residual stress at R2˜0.96). Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of shapes, boundary conditions and geometrical hierarchy.