We’re developing fast and scalable algorithms for solving partial differential equations that dynamically adjust the computation mesh in order to improve accuracy and make the best use of computational resources. We research new methods for block-structured adaptive mesh refinement and high-order unstructured curvilinear mesh optimization, targeting applications with moving and deforming meshes. Our algorithms can be used to accurately represent the moving and deforming geometry as well as to resolve internally moving features such as material interfaces, shocks, and reaction fronts. View content related to Mesh Management.
In the Variable Precision Computing (VPC) project, we develop algorithms and software to support the use of adaptive precision through the LLNL-developed floating-point compression algorithm, ZFP; explore techniques that combine adaptive layers of representation, similar to adaptive mesh refinement, using techniques similar to error transport and iterative refinement; and consider accumulating finite-precision errors in the context of new data representations.
The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.
For 20 years, scientists from LLNL’s Center for Applied Scientific Computing (CASC) have contributed scientific research and development in mathematics, computer science, and data science that have directly impacted national security and advanced basic science.
The Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory is developing algorithms and software technology to enable the application of structured adaptive mesh refinement (SAMR) to large-scale multi-physics problems relevant to U.S. Department of Energy programs. The SAMRAI (Structured Adaptive Mesh Refinement Application Infrastructure) library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with SAMR.
Brian Gunney became fascinated with the field of computational fluid dynamics because he thought it could be critical in solving many problems he considered unsolvable.