PROJECTS
Legacy
Quantum Molecular Dynamics
A new algorithm for use with first-principles molecular dynamics codes enables the number of atoms simulated to be proportional to the number of processors available.
Serpentine Wave Propagation
The Serpentine project develops advanced finite difference methods for solving hyperbolic wave propagation problems. Our approach is based on solving the governing equations in second order…
BLAST
Through research funded at LLNL, scientists have developed BLAST, a high-order finite element hydrodynamics research code that improves the accuracy of simulations, provides a path to extreme…
NEWS
CEED’s impact on Exascale Computing Project efforts is wide-ranging
The Center for Efficient Exascale Discretizations (CEED) within the ECP involves more than 30 computational scientists from 2 DOE labs (Livermore and Argonne) and 5 universities.
S&TR cover story: Machine learning on a mission
As demonstrated by CASC computer scientists, LLNL's innovative data-driven machine learning techniques teach computers to solve real-world problems.
CASC research showcased at major data science venues
LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.