End-to-End Deep Learning Approach to Predict Complex Stress and Strain Fields Directly from Microstructural Images

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. The deep learning model demonstrates not only the robustness of predicting multi-physical fields, scalability, and extensibility. The disclosure may alter physical modeling and simulations by incorporating material geometry and boundary conditions into a graphical representation, and vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.

Zhenze Yang

PhD student

My research interests include computational materials science, multiscale modeling and machine learning