# 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 (HPC) 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, time integration, scientific machine learning and reduced order modeling, 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, SUNDIALS time integrators and nonlinear solvers, libROM reduced order model toolbox, DOE’s Advanced Grid Modeling Program and the Exascale Computing Project.

## Group Lead

Erik Draeger: scalable scientific applications, quantum simulations, first-principles materials modeling, circulatory modeling

## Research Staff

Cody Balos: parallel computing, data-driven multiscale modeling and time-integration, numerical methods for PDEs, scientific software engineering

Robert Blake: multi-physics, multi-scale, numerical algorithms, parallel and distributed computing, scientific machine learning, compiler design and optimization, clinical simulations, cardiac simulations

Quan Bui: preconditioning techniques for multi-physics problems, multigrid methods, data-driven methods and machine learning for physics simulations

Siu Wun (Tony) Cheung: finite element methods, reduced order modeling, multi-scale methods, and scientific machine learning

Youngsoo Choi: model order reduction, surrogate modeling, mathematical optimization, numerical linear algebra, numerical PDEs, machine learning, multidisciplinary design optimization, and quantum computing

James Diffenderfer: numerical optimization (nonlinear programming, constrained optimization), machine learning, numerical analysis

David Gardner: multi-rate and implicit-explicit time integration methods, nonlinear solvers, multi-scale modeling, HPC, scientific software

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

Daniel Osei-Kuffuor: linear algebra and sparse matrix computations, numerical analysis and HPC, iterative solvers and preconditioners, variable precision computing, scalable algorithms for electronic structure calculations, performance portable scientific software design

Olga Pearce: parallel and distributed computing, parallel algorithms and optimizations, generic parallel libraries and tools

Lee Ricketson: numerical simulation of plasmas, particle-in-cell methods, finite volume methods, kinetic equations, sparse grid methods, Monte Carlo methods, multi-scale methods, stochastic differential equations

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

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