Past Projects

2021

Rapid high-fidelity aerothermal responses with quantified uncertainties via reduced-order modeling (PI)

  • Goal: This project aims to enable high-fidelity aerothermal simulations of hypersonic vehicles to be employed (1) to generate large databases with quantified uncertainties and (2) for rapid interactive simulation.
  • Sandia collaborators: Marco Arienti, David Ching, Jeff Fike, Micah Howard. 
  • External collaborators: Francesco Rizzi (NexGen), Karen Willcox (UT Austin).
  • Research topics: nonlinear model reduction; uncertainty quantification; hypersonic vehicles
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.

Revolutionizing systems-component design via advanced uncertainty quantification and reduced-order modeling

  • Goal: This project aims to enable rapid design evolution, concept exploration, and prototyping of complex system components while (1) ensuring designs satisfy all system-level requirements and (2) rigorously accounting for underlying uncertainties.
  • Sandia collaborators: John Tencer (PI), Marco Arienti, Erin Mussoni, Chi Hoang.
  • Research topics: nonlinear model reduction; uncertainty quantification; domain decomposition; component design
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.

2020

Algorithm development and verification for PRIME epidemic forecasting model

  • Goal: This project aims to extend the applicability of the PRIME epidemic forecasting model to better capture dynamics in multiple peak pandemic recovery scenarios. 
  • Sandia collaborators: Cosmin Safta (PI), Jaideep Ray.
  • Research topics: Markov Chain Monte Carlo (MCMC); COVID-19 Forecasting. 
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.
  • Project website: https://sandialabs.github.io/PRIME/

2019

On-line generation and error handling for surrogate models within multifidelity uncertainty quantification (PI) 

  • Goal: This project aims to integrate reduced-order model methods within a multifidelity uncertainty quantification framework and to demonstrate the greater efficiency and generality of this approach for several test problems with respect to their state-of-the-art counterparts.
  • Sandia collaborators: Gianluca Geraci (Co-PI), Mike Eldred, Kevin Carlberg.
  • External collaborators: Francesco Rizzi (NexGen).
  • Research topics: nonlinear model reduction; uncertainty quantification
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.