# CAREER: Multifidelity Scientific Machine Learning for Design

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Georgia Tech Research Corporation (GA) · $599,972

## Abstract

This Faculty Early Career Development (CAREER) award supports research looking to develop of a new approach to multifidelity scientific machine learning that combines data from both high- and low-fidelity simulations in a mathematically rigorous way, yielding new machine-learned models that issue high-accuracy predictions at low computational cost. In engineering design, predictive computational simulations enable design engineers to analyze the expected performance and cost of designs without needing to go through the expensive process of building and experimenting on physical prototypes. In typical settings, engineers have access to both high-fidelity simulations, which issue the most accurate predictions but at high computational cost, and low-fidelity simulations, which issue lower accuracy predictions more cheaply. If only high-fidelity simulations are used, the high computational cost of each simulation can limit the number of designs that can be considered, leading to sub-optimal designs. On the other hand, if only low-fidelity simulations are used, the low accuracy of the predictions can lead to less reliable designs. This CAREER award will support research focused on enabling engineers to create more optimal and robust designs across all engineering disciplines, ranging from space missions to biomedical devices to renewable energy systems, thereby advancing national defense, welfare, and prosperity. This award will also support the development of new educational modu

## Key facts

- **NSF award ID:** 2442140
- **Awardee organization:** Georgia Tech Research Corporation (GA)
- **SAM.gov UEI:** EMW9FC8J3HN4
- **PI:** Elizabeth Qian
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, Complex Systems, System Design and Simulation, WOMEN, MINORITY, DISABLED, NEC
- **Estimated total:** $599,972
- **Funds obligated:** $599,972
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 05/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2442140

## Citation

> US National Science Foundation, Award 2442140, CAREER: Multifidelity Scientific Machine Learning for Design. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2442140. Licensed CC0.

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