Modeling, Simulation, and Diagnostics Under Uncertainty

Researchers in this area study model generation, data science, simulation, and diagnostics to understand the existing state of the built environment (damages, geometry, service-life) and to understand the uncertainties of these parameters and how this uncertainty affects decision making. They also develop, validate, and apply new computational design tools that use simulation and optimization to guide architects and engineers in making engineering design decisions.

Research labs focused on this research area:

• Decision Analytics Integrating Simulations and Experiments (DAISE)
• Built Environment Analytics and Modeling Lab (BEAM Lab)
• Building Design Group (BDG)
• Sound Perception and Room Acoustics Laboratory
• Sustainable Buildings and Societies Laboratory

Current research in progress includes the following:

(funding bodies in parentheses; Penn State architectural engineering faculty names bolded)

• Developing adaptive material tools to improve the resiliency of in-service buildings (start-up funding), Rebecca Napolitano, JP Gevaudan
• Inverse design of existing envelope geometry and materials properties (start-up funding), Rebecca Napolitano, Wesley Reinhart
• Didactic framework to foster the adoption of new monitoring and analysis techniques in museums (start-up funding), Rebecca Napolitano
• Stochastic co-design for zero-carbon buildings (start-up funding), Greg Pavlak
• State-aware calibration of physics-based simulation models for structural applications (National Science Foundation), Sez Atamturktur, Andrew Brown, Chris Kitchens
• Subset-simulation for calibrating physics- and data-based simulation models (National Science Foundation), Sez Atamturktur, Andrew Brown
• Multi-fidelity model simulation, calibration, and validation framework (National Science Foundation), Sez Atamturktur
• Dimensionality reduction techniques for structural health monitoring (Eastern State Penitentiary), Rebecca Napolitano
• Investigation of incorporating virtual acoustics into high performance building virtual walk-throughs in the design process (GAANN Fellowship), Michelle Vigeant
• Building Energy Modeling — OpenStudio SDK Development and Management (U.S. Department of Energy via Pacific Northwest National Laboratory), Wangda Zuo
• Model Development for Virtual Electric Power Board (U.S. Department of Energy via Oak Ridge National Laboratory), Wangda Zuo
• OpenStudio Software Testing Methodologies Through Building Science Modeling and Analysis (U.S. Department of Energy via National Renewable Energy Laboratory), Wangda Zuo
• Artificial intelligence for large-scale power demand prediction (Start-up Funding), Wangda Zuo