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 microstructural materials in batteries
Our goal is to establish a digital twin of Li-ion battery microstructures to enable performance prediction and inverse optimization.
– Statistical characterization and stochastic reconstruction of digital microstructures to predict mechanical and electrochemical properties;
– Property prediction and inverse design based on machine learning;
– Manufacturing process simulation for electrode material design.
Deep learning-driven design of metamaterial systems considering manufacturing uncertainties
Supported by Ford University Research Program (URP)
– Deep learning-based discovery and design of cellular metamaterials to achieve superior performances (e.g. elastic wave trapping, impact energy absorption);
– Manufacturing process modeling to understand its impact on material microstructures and properties;
– 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.