My research focuses on methods to enable engineering design and analysis with large-scale computational models. These methods include:
- Sensitivity analysis of chaotic dynamical systems. The goal of this research is to enable efficient design optimization, error estimation, and uncertainty quantification of large scale chaotic systems like scale-resolving turbulent flow simulations.
- Reduced order modelling. The goal of this research is to exploit any underlying structure present in data generated by a "full order" large-scale computation model to construct a "reduced order" model that is accurate, robust, and significantly less costly.
Although the above methods are generally applicable, my main focus is on the following application spaces:
- Unsteady Aerodynamics and Turbulence
- Hypersonics and Aerothermodynamics
Recent Publication Highlights
- Check out our paper on a compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems. (Joint work with F. Rizzi, E. Parish, and J. Tencer)
- Check out our paper on forecasting multi-wave epidemics through Bayesian inference, with application to the COVID-19 pandemic in 2020. (Joint work with J. Ray and C. Safta.)
- Our AIAA journal paper on model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction is online! (Joint work with F. Rizzi, K. Carlberg, M. Howard, and J. Fike)
- PRIME code
- Story on Full Airframe Sensing Technology (FAST)
Check out this story about our new collaboration with University of Texas at Austin on sensing for hypersonic vehicles.
- Pressio is now live!
Pressio, a software package we are developing at Sandia to enable projection-based model reduction for nonlinear systems, is now available on github.