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

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of California-Berkeley (CA) · $917,946

## 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 storms. This research looks to develop novel tools that enable virtual models — called digital twins — to efficiently learn from data about infrastructure damage and recommend optimal response actions. By combining insights from mathematics and engineering, the project intends to make it possible to simulate and update infrastructure conditions in real time. This new capability can support faster recovery of power systems and improves health, safety, and economic stability. The project also strengthens STEM education through interdisciplinary training in civil engineering and mathematics, hands-on learning for undergraduates, and workshops for utility operators. In addition, it delivers open-source tools that can be reused and enhanced.

Power grids are increasingly vulnerable to sequences of extreme events, which complicate recovery and delay service restoration. This project investigates how to improve digital twin systems—virtual models that represent real infrastructure—by advancing three areas of science. First, it develops scalable methods for estimating unce

## Key facts

- **NSF award ID:** 2529327
- **Awardee organization:** University of California-Berkeley (CA)
- **SAM.gov UEI:** GS3YEVSS12N6
- **PI:** Luis A Ceferino
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CIVIL INFRASTRUCTURE, HAZARD AND DISASTER REDUCTION, HAZARD AND DISASTER RESPONSE, EARTHQUAKE ENGINEERING, Artificial Intelligence (AI), CIVIL INFRASTRUCTURE
- **Estimated total:** $917,946
- **Funds obligated:** $917,946
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2529327

## Citation

> US National Science Foundation, Award 2529327, MATH-DT: Scalable Bayesian Online Learning for Digital Twins in Power Network Resilience against Sequential Extreme Events. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2529327. Licensed CC0.

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