CDS&E/Collaborative Research: Physics-Informed Machine Learning for Tailoring the Multidir — NSF Award to University of Georgia Re
This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research project will contribute to the progress of science and the advancement of national prosperity by developing a framework for the inverse design and fabrication of multiphase composite materials with tailored mechanical properties.
| Award title | CDS&E/Collaborative Research: Physics-Informed Machine Learning for Tailoring the Multidir |
|---|---|
| Award ID | 2614637 |
| Awardee | University of Georgia Research Foundation Inc |
| City | ATHENS |
| State | GA |
| Amount obligated | $143,856 |
| Principal investigator | Yanyu Chen |
| Program | CDS&E |
| Start date | 10/01/2025 |
| Abstract | This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research project will contribute to the progress of science and the advancement of national prosperity by developing a framework for the inverse design and fabrication of multiphase composite materials with tailored mechanical properties. Despite recent advances in the deployment of machine learning techniques to materials science, the creation of materials with desired mechanical properties in multiple loading dir |
| Source | NSF Awards |
$799/mo
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