NSF award 2608887 · New York University

Stable formulations and numerical discretizations of loss functions for generative AI with applications to model reduction

Final report due

29 December 2029

in 1,168 days · The award ends on 31 August 2029.

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Extension request17 Jul 2029in 1003 days
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PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2029in 1038 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2029in 1168 days
award ending 2029-08-31 · 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 August 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2029.

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Award card

Award number: 2608887
Period of support: 1 Sep 2026 – 31 Aug 2029
Amount: $397,458
Awardee: New York University, NEW York, NY
Program: COMPUTATIONAL MATHEMATICS
Directorate: MPS

Abstract

Artificial intelligence is increasingly used to model complex scientific and engineering systems. Maintaining leadership in this area over the long term depends not only on larger models and more data but also on mathematical methods to enhance training and to make training more stable, reproducible, and mathematically well-founded. This project will develop mathematical principles for designing training objectives…

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