# Efficient high-order methods for radiative transfer via substructuring

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Syracuse University (NY) · $112,085

## Abstract

Many problems in science and engineering require predicting how radiation, light, or particles move through and interact with complex media. These problems arise in areas such as atmospheric science, optical imaging, nuclear engineering, and astrophysics, where accurate predictions are essential to scientific discovery, engineering design, and decision-making. A central tool for making such predictions is numerical simulation based on partial differential equations, which provides a first-principles way to model the relevant physical processes. However, these simulations remain very expensive because of the high-dimensional and multiscale nature of the underlying problems. This project aims to address this barrier by developing a systematic computational framework that integrates efficient high-order adaptive numerical methods, substructure-based parallel computation, and localized machine-learning models. The substructure-based design makes the computation well-suited for modern parallel computing systems, including high-performance computing clusters and GPUs. It also allows machine-learning models to be trained locally and inexpensively at the substructure level, while keeping the overall solver grounded in reliable and theoretically justified classical numerical methods. This combination of low-cost machine learning and first-principles-based numerical computation is expected to broaden access to the interdisciplinary area of scientific machine learning and provide studen

## Key facts

- **NSF award ID:** 2608769
- **Awardee organization:** Syracuse University (NY)
- **SAM.gov UEI:** C4BXLBC11LC6
- **PI:** Shukai Du
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, Advanced Manufacturing, COMPUTATIONAL SCIENCE & ENGING
- **Estimated total:** $112,085
- **Funds obligated:** $112,085
- **Transaction type:** Standard Grant
- **Period:** 09/01/2026 → 08/31/2029

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2608769

## Citation

> US National Science Foundation, Award 2608769, Efficient high-order methods for radiative transfer via substructuring. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2608769. Licensed CC0.

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