Feature Extraction for Synthesis and Design of Complex Material MicrostructuresCurrent students and collaborators: Ruijin Cang, Dr. Yang Jiao, Dr. Yongming Liu
Integrated Computational Materials Engineering (ICME) aims to develop scalable design methodologies for complex material systems by leveraging and extending knowledge from material science and design automation. The key challenges are (1) the high dimensional design representations of material structures and (2) the high computational cost for physics-based process-structure-property mappings. For (1), we develop unsupervised learning models that learn lower-dimensional feature spaces from where new designs (microstructures and topologies) can be created based on samples; for (2), we integrate deep learning and physics-based models to discover key factors (e.g., microstructure patterns) that influence properties of interest (e.g., fracture strength).
1. Cang, R., Xu, Y., Chen, S., Liu, Y., Jiao, Y., and Ren, Y. (2017). Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design. ASME Journal of Mechanical Design. arXiv , Source Code
2. Cang, R., and Ren, Y. (2017). Scalable Microstructure Reconstruction with Multi-scale Pattern Preservation. In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers. pdf