With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.
LLNL employees attended a five-part “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic knowledge of programming in Python.
Rushil Anirudh describes the machine learning field as undergoing a “gold rush.”
Marisa Torres, software developer with LLNL’s Global Security Computing Applications Division, combines her love of biology with coding.
Highlights include recent LDRD projects, Livermore Tomography Tools, our work with the open-source software community, fault recovery, and CEED.
Highlights include Computation’s annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.
Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.
LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.
Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.