most citedCan Time-Series Foundation Models Perform Building Energy Management Tasks?

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CE2025

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments

Maral Doctorarastoo, Katherine A. Flanigan, Mario Bergés +1

The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant chall…

cs.LG2025

Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects

Sizhe Ma, Katherine A. Flanigan, Mario Bergés +1

Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting…

cs.CV2025

Street View Sociability: Interpretable Analysis of Urban Social Behavior Across 15 Cities

Kieran Elrod, Katherine Flanigan, Mario Bergés

Designing socially active streets has long been a goal of urban planning, yet existing quantitative research largely measures pedestrian volume rather than the quality of social in…

cs.CE2025

Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer

Sizhe Ma, Katherine A. Flanigan, Mario Bergés

The advancement of the Internet of Things (IoT) and Artificial Intelligence has catalyzed the evolution of Digital Twins (DTs) from conceptual ideas to more implementable realities…

cs.LG20251 cited

Can Time-Series Foundation Models Perform Building Energy Management Tasks?

Ozan Baris Mulayim, Pengrui Quan, Liying Han +4

Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perf…

cs.LG2025

Bridging the Reality Gap in Digital Twins with Context-Aware, Physics-Guided Deep Learning

Sizhe Ma, Katherine A. Flanigan, Mario Bergés

Digital twins (DTs) enable powerful predictive analytics, but persistent discrepancies between simulations and real systems--known as the reality gap--undermine their reliability.…