Grants and Contracts Details
The main theme of our proposed project is to build novel material-specific learning architectures based on multi-scale "4D-microstructure" (in contrast to 3D-microstructure) representation space to discover the multi-scale material mechanisms for predictive material modeling and material design. The impact of the proposed project goes far beyond establishing high-fidelity predictive models for the specific material system of interest (i.e., corrosion crack evolution in metallic alloys). It inspires one to re-think the utility of machine learning in materials science: from knowledge-agnostic feature learning to reasoning mechanisms adaptive to domain-specific knowledge. The methodologies and frameworks for constructing novel physics-based learning models developed in this project can be readily applied to a variety of heterogeneous material systems.
|Effective start/end date||9/1/20 → 8/31/23|
- Arizona State University: $30,000.00
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.