NSF award 2625831 · Georgia Tech Research Corporation

Differentiable Reachability for Scalable Verification and Safe Training of Neural Network Controllers

Final report due

30 April 2030

in 1,290 days · The award ends on 31 December 2029.

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Decide about an extension2 Sep 2029in 1050 days
award ending 2029-12-31 · Research.gov
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Extension request16 Nov 2029in 1125 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Dec 2029in 1160 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due30 Apr 2030in 1290 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due30 Apr 2030in 1290 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out30 Apr 2030in 1290 days
award ending 2029-12-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 December 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 30 April 2030.

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Award card

Award number: 2625831
Period of support: 1 Jan 2027 – 31 Dec 2029
Amount: $495,000
Awardee: Georgia Tech Research Corporation, Atlanta, GA
Program: EPCL: Energy, Power, Control,
Directorate: ENG

Abstract

This NSF project aims to make it possible to mathematically guarantee the safe operation of modern automated systems that use learning and artificial intelligence to make control decisions. Robots, aircraft, and self-driving vehicles increasingly rely on machine-learning components, such as neural networks, to interpret their surroundings and decide how to act. But they also make it hard to prove that a system will…

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