Generalization Capabilities of Machine Learning for Solving Multiple Partial Differential — NSF Award to University of California-
This project develops a rigorous theoretical foundation for multi-operator learning, providing a mathematical framework to understand how neural networks can efficiently learn across collections of complex physical systems. Artificial intelligence (AI) research, and in particular deep learning, has made recent advances
| Award title | Generalization Capabilities of Machine Learning for Solving Multiple Partial Differential |
|---|---|
| Award ID | 2606034 |
| Awardee | University of California-Los Angeles |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $214,876 |
| Principal investigator | Hayden Schaeffer |
| Program | APPLIED MATHEMATICS |
| Start date | 10/01/2026 |
| Abstract | This project develops a rigorous theoretical foundation for multi-operator learning, providing a mathematical framework to understand how neural networks can efficiently learn across collections of complex physical systems. Artificial intelligence (AI) research, and in particular deep learning, has made recent advances in scientific computing, where empirical results outpace our theoretical understanding of why they work and how to design them reliably. This project addresses these questions by |
| Source | NSF Awards |
$799/mo
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