CAREER: Data-Driven Learning of Interpretable and Extrapolative Models of Complex Systems — NSF Award to University of Connecticut
Modern scientific and engineering challenges, from understanding cell growth to predicting material failure and crack formation under stress, require complex modeling and expensive experiments. While machine learning has demonstrated remarkable potential to accelerate scientific discovery for highly complex systems and
| Award title | CAREER: Data-Driven Learning of Interpretable and Extrapolative Models of Complex Systems |
|---|---|
| Award ID | 2544082 |
| Awardee | University of Connecticut |
| City | STORRS |
| State | CT |
| Amount obligated | $394,370 |
| Principal investigator | Qian Yang |
| Program | Info Integration & Informatics |
| Start date | 07/01/2026 |
| Abstract | Modern scientific and engineering challenges, from understanding cell growth to predicting material failure and crack formation under stress, require complex modeling and expensive experiments. While machine learning has demonstrated remarkable potential to accelerate scientific discovery for highly complex systems and reduce costs, its adoption in scientific research remains limited by a crucial bottleneck: the shortage of labeled training data. Obtaining large quantities of labeled data for sc |
| Source | NSF Awards |
$799/mo
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