# Energy-Stable Neural Basis Methods for Multiscale Porous Media Flow

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of Houston (TX) · $300,000

## 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 simulation of complex physical processes. However, existing approaches often lose accuracy and stability when applied to realistic multiscale systems. This project develops a new class of scientifically grounded artificial intelligence methods for reliable modeling of complex fluid flow and transport phenomena. The work advances foundational research at the intersection of computational mathematics, artificial intelligence, and scientific computing, supporting national priorities in AI-enabled scientific discovery and high-performance computing. The project will also train graduate and undergraduate students through research, mentoring, outreach, and open-source software development, strengthening the nation’s technical workforce in artificial intelligence and computational science. 
 
The project develops an energy-stable neural basis framework for multiscale partial differential equations arising in porous media flow and transport. The research addresses fundamental limitations in physic

## Key facts

- **NSF award ID:** 2608740
- **Awardee organization:** University of Houston (TX)
- **SAM.gov UEI:** QKWEF8XLMTT3
- **PI:** Min Wang
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, COMPUTATIONAL SCIENCE & ENGING
- **Estimated total:** $300,000
- **Funds obligated:** $300,000
- **Transaction type:** Standard Grant
- **Period:** 06/15/2026 → 05/31/2029

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2608740

## Citation

> US National Science Foundation, Award 2608740, Energy-Stable Neural Basis Methods for Multiscale Porous Media Flow. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2608740. Licensed CC0.

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