Randomized Algorithms for Operator Approximations in Sobolev Spaces — NSF Award to University of California-Los Angeles (CA, $330,
Machine learning and artificial intelligence has been successful in the approximation and prediction of complex physical phenomena. A key aspect is the development of models capable of capturing dependencies on input parameters, domain configurations, boundary conditions, initial states, and spacetime coordinates withi
| Award title | Randomized Algorithms for Operator Approximations in Sobolev Spaces |
|---|---|
| Award ID | 2514157 |
| Awardee | University of California-Los Angeles |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $330,000 |
| Principal investigator | Hayden Schaeffer |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 01/15/2026 |
| Abstract | Machine learning and artificial intelligence has been successful in the approximation and prediction of complex physical phenomena. A key aspect is the development of models capable of capturing dependencies on input parameters, domain configurations, boundary conditions, initial states, and spacetime coordinates within one neural network. One approach is operator learning, which encodes the solution operators of parametric partial differential equations into neural networks. However, the size o |
| Source | NSF Awards |
$799/mo
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