with innovative models and methods that can predict structural responses to natural hazards and extreme loads.
We focus on challenges that require a fusion of data analytics, structural dynamics and mechanics, and computational or numerical models. Whether analyzing the US Navy’s fleet or skyscrapers in California, we use data to maximize the balance between efficiency and accuracy, tapping both high-fidelity and lower-fidelity models.
Uncovering the governing physics of friction-based seismic protective systems and base-isolation devices
Exploring techniques for improved estimation of structural features and modal characteristics of buildings, bridges, and aircraft from operational response data
Developing and deploying numerical methods and machine learning tools for selecting, calibrating, and validating models
Rapidly identifying transitions of a structural response to a nonlinear regime as a possible indicator of damage
Combining physics-based models and machine learning to identify structures from signals and leveraging diverse data types and model fidelities to create more confident predictions
Implementing advanced computational tools to discover how microstructural features influence macrostructural properties