This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports fundamental research to improve the generalization capabilities of digital twin models for complex systems. The $269,187 grant will enable researchers at Smith College to develop hybrid digital twin architectures that combine physics-based and domain-agnostic components, allowing for improved predictive performance across a range of conditions, including extreme or unexpected scenarios. The project aims to quantify the relationships between model architecture, data availability, and out-of-distribution generalization, with a particular focus on applications related to human circadian rhythms. The research will provide valuable training opportunities for undergraduate and graduate students in mathematical modeling, data analysis, and machine learning. The outcomes are expected to guide the creation of more robust digital twins and inform critical decision-making under new or uncertain conditions.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $269.2k | 8/28/24 |