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 Earth system 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: 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: HPC, scientific software, numerical time integration methods, multi-scale modeling, scalable algorithms, scientific machine learning

Justin Dong: scientific machine learning, physics-informed learning, hybrid PDE-ML models, finite element methods

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

Stefanie Guenther: PDE-constrained optimization, optimal control for machine learning, optimal control for quantum computing, adjoints and automatic differentiation

Nathan Keilbart: high-throughput workflows, HPC, electronic structure calculations of materials, Density Functional Theory

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

Anders Petersson: numerical methods for large-scale wave propagation problems, ODE and PDE constrained optimization, optimal control of quantum systems, seismic wave propagation

Steven Roberts: time integration for multiphysics problems, numerical analysis for differential equations, scientific software, scientific machine learning, symbolic computation

Robert Stephany: system Identification, reduced order modeling, neural ODEs, differential equation constrained optimization, scientific machine learning

Paul Tranquilli: time integration methods, algorithm analysis, theorem-proving, reduced order modeling

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