Current Projects

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, Chi Hoang.
  • Research topics: nonlinear model reduction; uncertainty quantification; domain decomposition; component design
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.

Pressio: Projection-based model reduction for large-scale nonlinear dynamical systems

  • Goal: This project aims to enable parallel, scalable, and performant projection-based model reduction capabilities to be adopted by any C++ application in a minimally intrusive manner with Pressio, an open-source C++11 header-only library.
  • Sandia collaborators: Jaideep Ray (PI), Francesco Rizzi (lead developer), Mark Hoemmen, Eric Parish, Kenny Chowdhary.
  • Research topics: nonlinear model reduction; high performance computing
  • Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program. 

Algorithm development and verification for COMBO epidemic forecasting model

  • Goal: This project aims to extend the applicability of the COMBO 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.