NSF award 2510166 · Stanford University

Branching processes, partial differential equations and applications to knowledge diffusion

Final report due

29 December 2028

in 802 days · The award ends on 31 August 2028.

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 extension3 May 2028in 562 days
award ending 2028-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2028in 637 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2028in 672 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2028in 802 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2028in 802 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2028in 802 days
award ending 2028-08-31 · 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 31 August 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2028.

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: 2510166
Period of support: 1 Sep 2025 – 31 Aug 2028
Amount: $300,000
Awardee: Stanford University, Stanford, CA
Program: APPLIED MATHEMATICS
Directorate: MPS

Abstract

Dynamics in heterogeneous and random environments arise in a broad range of scientific fields, from economic growth and fluid turbulence to diffusion models in machine learning. These systems are often modelled by nonlinear partial differential equations and stochastic processes involving multiple temporal and spatial scales. To mitigate this complexity, researchers frequently use approximate macroscopic effective…

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.