# Collaborative Research: Scientific Computing Assisted Machine Learning for Wave Imaging

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of North Carolina at Chapel Hill (NC) · $220,000

## Abstract

Computational wave imaging, vital for uncovering hidden properties in diverse fields of science and engineering, such as materials science, medicine, and geoscience, faces significant challenges. Traditional methods struggle with the inherent complexity and computational demands of such problems. Although deep learning offers promise for these scientific inverse problems, its efficacy is hindered by the scarcity of labeled data, often due to costly experiments and expertise requirements. This underscores the need for innovative approaches that circumvent data limitations in wave imaging. This project seeks to optimize the potential of deep learning in computational wave imaging by introducing techniques to address data scarcity and improve generalizability, aiming to drastically lessen deep learning's dependence on extensive labeled datasets, efficiently generate high-quality training data, and greatly improve deep learning's capacity to solve real-world problems. It also emphasizes educational integration and interdisciplinary collaboration, and promotes the sharing of open-source computer codes and datasets, enhancing the broader scientific community’s ability to conduct research and providing educators with valuable tools for teaching computational and data-enabled science, engineering, and mathematics.
 
Physical principles will be integrated with advanced deep learning models in hybrid learning strategies. Hybrid strategies involve efficient wave simulations results 

## Key facts

- **NSF award ID:** 2504439
- **Awardee organization:** University of North Carolina at Chapel Hill (NC)
- **SAM.gov UEI:** D3LHU66KBLD5
- **PI:** Youzuo Lin
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** COMPUTATIONAL SCIENCE & ENGING, Machine Learning Theory, Artificial Intelligence (AI)
- **Estimated total:** $220,000
- **Funds obligated:** $220,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2504439

## Citation

> US National Science Foundation, Award 2504439, Collaborative Research: Scientific Computing Assisted Machine Learning for Wave Imaging. Retrieved via AI Analytics 2026-07-19 from https://api.ai-analytics.org/grant/nsf/2504439. Licensed CC0.

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