Center for Applied Scientific Computing
Scientific Computing Group

Members of the Scientific Computing Group develop new and efficient numerical algorithms, techniques, and methodologies for solving scientific problems on high-performance computing systems. Our research is in a spectrum of areas of vital interest to LLNL, and we work in close collaboration with Laboratory programs, other groups within CASC, and other national laboratories and universities. Application areas of current interest/expertise include climate modeling, subsurface flow modeling, mathematical and computational biology, power grid simulation, computational fluid dynamics, transport models, first-principles molecular dynamics, and plasma modeling for both ICF and magnetic fusion.  We actively investigate, apply, and develop new methods in multi-physics and multi-scale modeling, mathematical optimization, structured adaptive mesh refinement, ALE-AMR techniques, high-order discretizations, nonlinear systems and solvers, and variable precision computing.

To learn more about what we do, we invite you to look at some of the current projects to which our group members contribute: Climate Modeling, Linear Scaling Electronic Structure Calculations, Structured Adaptive Mesh Refinement (SAMRAI), SUNDIALS time integrators and nonlinear solvers, the Edge Simulation Laboratory, and the Variable Precision Computing Project.

Some of our past projects include Extreme Resilient Discretizations, Parflow, Phase Field Modeling, and Extreme Materials at Extreme Scales (ExMatEx).

Group Lead

Jeff Hittinger: hydrodynamics, computational plasma physics, adaptive mesh refinement, high-order finite volume methods, verification and a posteriori error estimation, variable precision computing

Research Staff

Robert Anderson: hydrodynamics, computational physics, adaptive mesh refinement

Milo Dorr: scientific computing, numerical analysis, parallel processing, plasma models, multigrid methods

Jean-Luc Fattebert: first-principles molecular dynamics algorithms, large scale eigenvalue problems, parallel scientific computing, phase-field modeling

David Gardner: multi-scale modeling, implicit-explicit time integration, nonlinear solvers, parallel computing

Debojyoti Ghosh: numerical methods for hyperbolic partial differential equations, finite-difference and finite-volume methods, implicit-explicit time integration, compressible flows, scalable algorithms

Matthieu Benoit Lecouvez: numerical and functional analysis, domain decomposition methods, ordinary, partial and algebraic differential equations, linear solvers

John Loffeld: exponential integrators, numerical analysis, parallel computing, object-oriented software design

Daniel Osei-Kuffuor: preconditioning techniques for highly indefinite linear systems

Slaven Peles: complex systems, differential-algebraic equations, uncertainty quantification, model verification and validation, multi-scale modeling, implicit-explicit time integration, nonlinear solvers, parallel computing

Cosmin Petra: large-scale numerical optimization, stochastic programming, parallel algorithms for the optimization of complex energy systems under uncertainty.

Deepak Rajan: computational optimization and integer programming; optimization problems in scientific distributed computing

Claudio Santiago: combinatorial optimization, integer programming, conic programming, convex relaxations, and nonlinear programming

Steve Smith: object-oriented software design, adaptive mesh refinement, subsurface modeling

Liam Stanton: continuum modeling and nonlinear dynamics in materials science, electrochemistry and plasmas

Tanya Vassilevska: mathematical and computational biology

Christopher Vogl: numerical methods for partial differential equations, adaptive mesh refinement, level set methods, lipid bi-layer vesicle modeling, tsunami simulation

Carol Woodward: nonlinear solvers, implicit PDE methods, verification, parallel computing, flow through porous media, numerical error estimation