Collaborative Research: Geometric Scientific Machine Learning for PDEs with Tensorial Cons — NSF Award to University of Illinois a
As artificial intelligence (AI) increasingly accelerates scientific discovery and engineering design, there is a growing need for models that are not only computationally fast but physically reliable. Many current AI approaches rely purely on massive datasets, predicting physical phenomena without incorporating the und
| Award title | Collaborative Research: Geometric Scientific Machine Learning for PDEs with Tensorial Cons |
|---|---|
| Award ID | 2608775 |
| Awardee | University of Illinois at Urbana-Champaign |
| City | URBANA |
| State | IL |
| Amount obligated | $349,985 |
| Principal investigator | Anil Hirani |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 06/15/2026 |
| Abstract | As artificial intelligence (AI) increasingly accelerates scientific discovery and engineering design, there is a growing need for models that are not only computationally fast but physically reliable. Many current AI approaches rely purely on massive datasets, predicting physical phenomena without incorporating the underlying laws of nature. This purely data-driven approach can lead to predictions that are unstable or physically impossible. This project develops 'physics-preserving' machine lear |
| Source | NSF Awards |
$799/mo
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