Skip to the ledger

GrantClerk

NSF award 2608775 · University of Illinois at Urbana-Champaign

Collaborative Research: Geometric Scientific Machine Learning for PDEs with Tensorial Constraints

Final report due

28 September 2029

in 1,076 days · The award ends on 31 May 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 extension31 Jan 2029in 836 days

    award ending 2029-05-31 · Research.gov

  • Extension request16 Apr 2029in 911 days

    award ending 2029-05-31 · Research.gov

  • No-cost extension notice21 May 2029in 946 days

    award ending 2029-05-31 · Research.gov

  • Final report due28 Sep 2029in 1076 days

    award ending 2029-05-31 · Research.gov

  • Project outcomes report due28 Sep 2029in 1076 days

    award ending 2029-05-31 · Research.gov

  • Award is closed out28 Sep 2029in 1076 days

    award ending 2029-05-31 · Research.gov

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

  1. NSF lists this award's end date as 31 May 2029.
  2. NSF rules say the final report is due no later than 120 days after that.
  3. So the final report is due by 28 September 2029.

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
2608775
Period of support
15 Jun 2026 – 31 May 2029
Amount
$349,985
Awardee
University of Illinois at Urbana-Champaign, Urbana, IL
Program
COMPUTATIONAL MATHEMATICS
Directorate
MPS

Abstract

As artificial intelligence (AI) increasingly accelerates scientific discovery and engineering design, there is a growing need for models that are not only computationally fast but physically reliable. Many current AI approaches rely purely on massive datasets, predicting physical phenomena without incorporating the underlying laws of nature. This purely data-driven approach can lead to predictions that are unstable…

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.