Kevin Carlberg

Current Projects

Rigorous surrogates for quantifying margins of uncertainty (PI)

  • Goal: This project aims to develop fundamental nonlinear model-reduction techniques to enable orders-of-magnitude reduction in the computational resources needed to simulate large-scale dynamical systems in an uncertainty-quantification context.
  • Sandia collaborators: Eric Parish, Brian Freno, Kookjin Lee, Chi Hoang.
  • External collaborators: Francesco Rizzi (NexGen), Philip Etter (Stanford), Ricardo Baptista (MIT), Wayne Uy (Cornell Univ), Matthias Morzfeld (Univ Arizona), Fei Lu (LBNL), Stefano Pagani (Polytechnic University of Milan), Andrea Manzoni (Polytechnic University of Milan).
  • Research topics: nonlinear model reduction; uncertainty quantification; high-performance computing; domain decomposition; adaptive refinement; Bayesian inference; machine learning
  • Funding source: National Nuclear Security Administration, Advanced Simulation and Computing (ASC), Verification & Validation Methods.

Advanced ROM methods for thermal/mechanical responses (PI)

  • Goal: The objective of this project is to develop robust nonlinear model-reduction methods to simulate the thermal/mechanical failure of complex engineering systems in different configurations along with non-intrusivity.
  • Sandia collaborators: John Tencer, Chi Hoang, Flint Pierce.
  • External collaborators: Francesco Rizzi (NexGen), Freddie Witherden (Texas A&M), Antony Jameson (Texas A&M), Jeremy Morton (Stanford), Mykel Kochenderfer (Stanford), Zhe Bai (Univ of Washington), Steven Brunton (Univ of Washington)
  • Research topics: nonlinear model reduction; non-intrusive methods; uncertainty quantification; high-performance computing; machine learning
  • Funding source: National Nuclear Security Administration, Advanced Simulation and Computing (ASC), Verification & Validation Methods.

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

  • Goal: This project aimes 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: Micah Howard, Patrick Blonigan.
  • 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 (PI)

  • 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: Patrick Blonigan, John Tencer, Bradley Bon, Camron Proctor.
  • Research topics: nonlinear model reduction; uncertainty quantification; domain decomposition; component design
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.

Agile ‘Lego-like’ full-system design with domain-decomposition uncertainty quantification and reduced-order modeling (PI)

  • Goal: This project will enable agile uncertainty propagation for engineering systems while avoiding full-system meshing or deterministic solves. The approach comprises (1) precomputing a ‘component library’ consisting of a mesh, a high-fidelity discretization, and a reduced-order model for each component, (2) modeling the system as a network of components, and (3) rapidly propagating uncertainties throughout the system via domain-decomposition uncertainty quantification.
  • Sandia collaborators: John Tencer
  • Research topics: nonlinear model reduction; uncertainty quantification; domain decomposition; component design
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.