Structure-preserving machine learning moment closures for kinetic equations — NSF Award to University of Delaware (DE, $158,271)
Kinetic theory describes the behaviors of dynamic systems from a statistical point of view. It has wide applications in many fields, including supersonic flows, microelectromechanical systems, unconventional gas reservoirs, space vehicle re-entry problems, and nuclear fusion. Because of the high dimensionality of such
| Award title | Structure-preserving machine learning moment closures for kinetic equations |
|---|---|
| Award ID | 2618114 |
| Awardee | University of Delaware |
| City | NEWARK |
| State | DE |
| Amount obligated | $158,271 |
| Principal investigator | Juntao Huang |
| Program | OFFICE OF MULTIDISCIPLINARY AC, COMPUTATIONAL MATHEMATICS |
| Start date | 02/15/2026 |
| Abstract | Kinetic theory describes the behaviors of dynamic systems from a statistical point of view. It has wide applications in many fields, including supersonic flows, microelectromechanical systems, unconventional gas reservoirs, space vehicle re-entry problems, and nuclear fusion. Because of the high dimensionality of such models, efficient simulation is a long-standing challenge, which limits their applications to real-world problems. This research project will address this challenge by developing r |
| Source | NSF Awards |
$799/mo
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