Energy-Stable Neural Basis Methods for Multiscale Porous Media Flow — NSF Award to University of Houston (TX, $300,000)
Many important physical systems involve complex processes spanning multiple physical scales, including those arising in carbon storage, hydrogen containment, and groundwater management. Accurate prediction of these systems is essential for sustainable energy technologies, environmental protection, and national economic
| Award title | Energy-Stable Neural Basis Methods for Multiscale Porous Media Flow |
|---|---|
| Award ID | 2608740 |
| Awardee | University of Houston |
| City | HOUSTON |
| State | TX |
| Amount obligated | $300,000 |
| Principal investigator | Min Wang |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 06/15/2026 |
| Abstract | Many important physical systems involve complex processes spanning multiple physical scales, including those arising in carbon storage, hydrogen containment, and groundwater management. Accurate prediction of these systems is essential for sustainable energy technologies, environmental protection, and national economic competitiveness. Over the past decade, physics-informed artificial intelligence methods have shown strong potential for accelerating scientific discovery and enabling rapid simula |
| Source | NSF Awards |
$799/mo
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