Our applied mathematicians work with domain scientists to develop state-of-the-art algorithms and methods for finite volume and discontinuous Galerkin schemes, level set methods and cut cell methods for modeling interfaces, specialized methods for stochastic differential equations, particle and particle-mesh algorithms such as Particle-in-Cell (PIC) and Particle-Particle-Particle-Mesh (PPPM), graph and combinatorial algorithms, discrete event simulation, adaptive mesh refinement and a broad range of approaches for coupling algorithms with different physics at different scales.
Algoim is a collection of high-order accurate numerical methods and C++ algorithms for working with implicitly-defined geometry and level set methods. Contact: Robert Saye
Our Machine Learning Development Team is using machine learning models within an AMReX framework to accelerate the time-to-solution of computational kernels without reducing accuracy. Contact: Andy Nonaka
PFASST is an algorithm designed to provide parallelism in the time direction for the numerical solution of ordinary and partial differential equations. Contact: Xiaoye Sherry Li
