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
- Our preprint on Pressio, a new software package enabling projection-based model reduction for large-scale nonlinear dynamical systems, is availible on the arXiv. (Joint work with F. Rizzi and K. Carlberg)
- Our AIAA conference paper on model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction is online. (Joint work with K. Carlberg, F. Rizzi, M. Howard, and J. Fike)
- Our AIAA conference paper on our work towards an integrated and efficient framework for leveraging reduced order models for multifidelity uncertainty quantification is online. (Joint work with G. Geraci, F. Rizzi, and M. Eldred)
- 07/10/20: Check out my talk at the 2020 SIAM annual meeting: "Pressio: A computational framework enabling projection-based model reduction for large-scale nonlinear dynamical systems". My presentation starts at 1:42:48.
- Pressio, a software package we are developing at Sandia to enable projection-based model reduction for nonlinear systems, is now availible on github.