Advanced manufacturing technologies, such as additive manufacturing, have revolutionized materials system design and functional applications. To effectively harness these advancements, our research centers on integrated computational materials science and engineering (ICMSE), which enables a deep understanding of the process-structure-property-performance relationship. Specifically, we employ innovative computational methods, including data-driven and machine learning tools, to model the behavior of metal alloys, polymers, and composite materials from processing to performance.

Composite materials possess a hierarchical structure that necessitates multiresolution analysis to comprehend their failure and performance characteristics. However, traditional approaches struggle to simultaneously examine micro-scale physics and part-scale behavior due to the immense computational requirements involved. To tackle these challenges, we have developed mechanistic data-driven reduced-order modeling techniques, including Self-consistent Clustering Analysis (SCA), Multiresolution-Clustering Analysis (MCA), mechanistic data science (MDS), and Tensor Decomposition (TD). These methodologies facilitate concurrent multiresolution analysis of composite materials, enabling the exploration of their non-linear behavior and accurate prediction of structural performance under diverse loading conditions.