Computational Urban Sciences

The Computational Urban Sciences Group at Oak Ridge National Laboratory brings together a diverse team of scientists dedicated to developing cutting-edge solutions through AI, digital twin models, and high-performance computing. The group focuses on advancing the science of integrated urban systems—including energy, water, transportation, built and natural environments, and policy—under evolving operational conditions and extreme weather events.

The group’s guiding principle is “if it can happen in urban areas, then we study the science behind it.”

Research Focus

Our work is organized around four core research areas, each of which addresses key challenges in the evolving urban landscape:

1. Urban Networks
   The group develops advanced tools to monitor, analyze, and enhance the reliability and resilience of interconnected urban infrastructure systems.
2. Urban Disasters
   The group pioneers new data-driven methods, modeling approaches, and visualization techniques to understand and mitigate cascading impacts of natural and human-made disasters.
3. Urban Health
   The group designs and implements innovative tools and models to predict, assess, and reduce the effects of urban environments on public health.
4. Urban Data
   The group integrates cutting-edge technologies into urban settings to generate high-resolution data, thereby revealing emerging risks and shaping proactive responses.

Featured Capabilities

• Oak Ridge-Siting Analysis for power Generation Expansion (OR-SAGE)
  As the United States builds next-generation facilities—such as data centers and advanced/small modular reactor power plants—the OR-SAGE tool supports companies by accelerating vital predevelopment planning, thereby streamlining efforts that would otherwise take years to accomplish independently.
• Resiliency and Emission Control Through Optimizing Intermodal Logistics (RECOIL)
  In partnership with the University of Tennessee, Knoxville, our group is developing RECOIL, a cognitive digital twin of the US intermodal freight transportation system. This virtual model spans roads, railways, and waterways to optimize operational efficiency and system resilience.
• Hazard Detection in Urban Information Technology Systems
  As urban areas increasingly depend on information technology to capture vital data and critical events for informed decision-making, the group is developing innovative methods, akin to medical records, to detect hazards within these systems—hazards that could compromise data quality and reliability. This work is being conducted in collaboration with the US Department of Veterans Affairs.
• High-Resolution, Large-Scale Flood Inundation Mapping and Analysis
  Enhancing regional resilience is essential for making informed decisions about planning, building, and maintaining critical infrastructure. The group has developed an interdisciplinary scientific computing framework that can assess flood inundation risks at regional and continental scales. This framework produces high-resolution hydro terrain and flood inundation maps and analytics to support situational awareness of existing critical infrastructures and inform decision-making for strategic infrastructure planning and management.