# MATH-DT: Heterogeneous transfer and federated learning for digital twin in unmanned aerial vehicles

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · OHIO STATE UNIVERSITY, THE (OH) · $570,000

## Abstract

In recent years, the ambitious concept of Digital Twin (DT), with the aspiration of creating a virtual representation of a complex asset or process, has received enormous interest from industry leaders, engineers, policymakers, and scientists. The DT promises to digitally replicate the high-dimensional, multi-modal, and dynamic physical systems and assist decision-making with reliable predictions. For example, in aerospace applications, a DT that can accurately simulate flight operational conditions based on data and feedback from the “flying twin” has great potential to improve aerial vehicles' safety, reliability, and control, especially in extreme service conditions. However, the development of DTs has been largely ad hoc, and generalizable mathematical foundations are poorly understood. The investigators aim to develop Artificial Intelligence (AI) solutions in terms of heterogeneous transfer and personalized federated learning to solve mathematical and computing challenges in developing a DT for Unmanned Aerial Vehicles (UAVs). An interdisciplinary approach is taken that combines expertise in Statistics, Aerospace Engineering, and Computer Science to develop a holistic statistical and computational framework that is useful for the development of DTs generally, even beyond aerospace applications.  The team will undertake educational outreach programs in collaboration with university partners. The team will also teach and mentor middle school and high school students in a 

## Key facts

- **NSF award ID:** 2529302
- **Awardee organization:** OHIO STATE UNIVERSITY, THE (OH)
- **SAM.gov UEI:** DLWBSLWAJWR1
- **PI:** Subhadeep Paul
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory
- **Estimated total:** $570,000
- **Funds obligated:** $570,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2529302

## Citation

> US National Science Foundation, Award 2529302, MATH-DT: Heterogeneous transfer and federated learning for digital twin in unmanned aerial vehicles. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2529302. Licensed CC0.

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