Stochastic modeling and computational design of microstructural materials

The purpose is to develop computational methods to enable microscopic image-based statistical characterization, stochastic reconstruction, numerical modeling, and uncertainty quantification of the heterogeneous microstructural materials. In addition, we are also interested in physics-informed statistical learning methods to discover the processing-microstructure-property relation for computational material design.

Computational modeling of porous microstructural materials in energy storageĀ systems
In collaboration with MIT Battery Consortium
– Statistical characterization of microstructure characteristics based on microscopic image processing;
– Stochastic reconstruction of digital microstructures to predict properties of interest.


Design of mesostructure-structure systems for Additive Manufacturing
Supported by Ford University Research Program (URP)
– Deep learning-based discovery and design of cellular metamaterials to achieve superior performances (e.g. impact energy absorption);
– Multi-fidelity information fusion to improve the efficiency of design evaluation;
– Uncertainty quantification of the spatially-correlation random quantities in complex topological structures.
Uncertainty quantification, uncertainty propagation, and design for reliability/robustness
The purpose is to provide computational methods (enablers) for the aforementioned research topics.