Current Projects

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

  • 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 collaborator: Eric Parish.
  • External collaborators: Francesco Rizzi (NexGen, lead developer), Mikolaj Zuzek (NexGen).
  • Research topics: nonlinear model reduction; high performance computing
  • Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program. 
  • Project Website: https://pressio.github.io/

Rigorous Surrogates for Quantifying Model Uncertainty

  • Goal: This project aims to develop novel model reduction methods for nonlinear computational simulations.
  • Sandia collaborators: Eric Parish (PI), Elizabeth Krath, Chi Hoang, Yuki Shimizu.
  • Research topics: nonlinear model reduction; error estimation
  • Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program. 

Full-Airframe Sensing Technology for Hypersonic Aerodynamics Measurements

  • Goal: Develop scientific machine learning approaches to infer the pressure distribution on a hypersonic flight vehicle given internal strain gauge and thermocouple readings
  • External collaborators (selected): Noel Clemens (UT Austin, PI), Karen Willcox (UT Austin), Julie Pham (UT Austin), Carlos Cesnik (U Michigan)
  • Research topics: scientific machine learning; inverse problems; model order reduction; aerothermodynamics modeling
  • Funding source: AFOSR/NASA University Leadership Initiative
  • Project website: https://fast.ae.utexas.edu/

Data propagation components for the Sandia Parallel Aerodynamics and Reentry Code

  • Goal: Implement an adjoint capability in the Sandia Parallel Aerodynamics and Reentry Code in support of inverse problems and design optimization.
  • Sandia collaborators: Eric Phipps (PI), Jaideep Ray, Kathryn Maupin, Denis Ridzal
  • Research topics: adjoint methods; inverse problems, high performance computing
  • Funding source: Sandia National Laboratories’ Advanced Simulation and Computing Advanced Technology Development and Mitigation program.