NSF award 2529327 · University of California-Berkeley

MATH-DT: Scalable Bayesian Online Learning for Digital Twins in Power Network Resilience against Sequential Extreme Events

Final report due

29 December 2028

in 803 days · The award ends on 31 August 2028.

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award ending 2028-08-31 · Research.gov
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Extension request17 Jul 2028in 638 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 673 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 803 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 803 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 803 days
award ending 2028-08-31 · 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 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.

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Award card

Award number: 2529327
Period of support: 1 Sep 2025 – 31 Aug 2028
Amount: $917,946
Awardee: University of California-Berkeley, Berkeley, CA
Program: APPLIED MATHEMATICS, Special Initiatives
Directorate: ENG

Abstract

The objective of this Mathematical Foundations of Digital Twins (MATH-DT) project is to support research on building smarter models of power networks to help them recover more quickly after disasters. Natural hazards like hurricanes and earthquakes often lead to widespread and prolonged power outages. These outages are especially hard to manage when disasters occur in sequence, such as aftershocks or back-to-back…

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