Grants and Contracts Details
Description
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.
Status | Finished |
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Effective start/end date | 9/1/20 → 8/31/23 |
Funding
- Arizona State University: $30,000.00
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