NSF award 2608930 · University of Alabama Tuscaloosa

Advanced Neural Network-Enhanced Reduced Order Modeling for Solving PDEs with High-Dimensionality, Randomness, and Multiscale Features

Final report due

28 January 2030

in 1,198 days · The award ends on 30 September 2029.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Jun 2029in 958 days
award ending 2029-09-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 Aug 2029in 1033 days
award ending 2029-09-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2029in 1068 days
award ending 2029-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2030in 1198 days
award ending 2029-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2030in 1198 days
award ending 2029-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2030in 1198 days
award ending 2029-09-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 30 September 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 January 2030.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2608930
Period of support: 1 Oct 2026 – 30 Sep 2029
Amount: $229,586
Awardee: University of Alabama Tuscaloosa, Tuscaloosa, AL
Program: COMPUTATIONAL MATHEMATICS
Directorate: MPS

Abstract

Many challenges in science and engineering require fast and reliable predictions of how physical systems change over time, such as heat moving through a room, gas spreading through air, fluid flowing through complex materials, or environmental conditions changing around an autonomous robot. These predictions are often made by solving partial differential equations, but the calculations can become too slow or…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.