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: Ray Tuminaro, Drew Kouri, Brian Freno.
  • External collaborators: Kookjin Lee (Univ of Maryland), Howard Elman (Univ of Maryland), Wayne Uy (Cornell Univ), Matthew Zahr (Stanford), Sumeet Trehan (Stanford), Louis Durlofsky (Stanford), Lukas Brencher (Univ Stuttgart), Bernard Haasdonk (Univ Stuttgart), Matthias Morzfeld (Univ Arizona), Fei Lu (LBNL).
  • Research topics: nonlinear model reduction; uncertainty quantification; time parallelism; high-performance computing; adaptive refinement; Bayesian inference; machine learning; stochastic optimization
  • Funding source: National Nuclear Security Administration, Advanced Simulation and Computing (ASC), Verification & Validation Methods.

Subsystem ROM and UQ for rapid, agile, extreme-scale simulation (PI)

  • Goal: This project aims to enable a divide-and-conquer strategy for uncertainty quantification and model reduction for decomposable systems at extreme-scale.
  • Sandia collaborators: Khachik Sargsyan, Mohammad Khalil, Youngsoo Choi, Chi Hoang, Ray Tuminaro, Radoslav Bozinoski, Ali Pinar.
  • External collaborators: Sofia Guzzetti (Emory Univ), Jiahua Jiang (Univ of Massachusetts Dartmouth), H Cagan Ozen (Columbia Univ), Susie Sargsyan (Univ of Washington).
  • Research topics: nonlinear model reduction; uncertainty quantification; domain decomposition; high-performance computing; networks; multigrid; Bayesian inference
  • Funding source: Sandia National Laboratories’ Laboratory-Directed Research & Development.

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.
  • Sandia collaborators: Roy Hogan, Jeffrey Fike, Liqian Peng, Chi Hoang.
  • External collaborators: Zhe Bai (Univ of Washington), Steven Brunton (Univ of Washington)
  • Research topics: nonlinear model reduction; uncertainty quantification; time parallelism; high-performance computing; finite element analysis; structure preservation
  • Funding source: National Nuclear Security Administration, Advanced Simulation and Computing (ASC), Verification & Validation Methods.

Rapid Bayesian inferences for seismic wave propagation (PI)

  • Goal: This project aims to enable near-real-time Bayesian inference for the detection of low-magnitude seimic events using advanced methods from Bayesian inference, model reduction, and epistemic uncertainty quantification
  • Sandia collaborators: Thomas Catanach, Khachik Sargsyan
  • Research topics: Bayesian inference; uncertainty quantification; model reduction
  • Funding source: National Nuclear Security Administration, Nonproliferation Research and Development (NA-22).