3 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
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…
Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer +4
Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC),…
Semantic Technologies in Practical Demand Response: An Informational Requirement-based Roadmap
Ozan Baris Mulayim, Anand Krishnan Prakash, Yuvraj Agarwal +5
The transition to a modern and efficient future grid relies on the seamless coordination of distributed energy resources and applications such as Demand Response (DR). While this t…
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…
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…
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…
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.…