Topic: Data Science

Simulation workflows for ALE methods often require a manual tuning process. We are developing novel predictive analytics for simulations and an infrastructure for integration of analytics.

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With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.

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Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

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Rushil Anirudh describes the machine learning field as undergoing a “gold rush.”

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AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.

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Marisa Torres, software developer with LLNL’s Global Security Computing Applications Division, combines her love of biology with coding.

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Highlights include the directorate'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.

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SOAR (Stateless, One-pass Adaptive Refinement) is a view-dependent mesh refinement and rendering algorithm.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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At just 5 years old, Marisol Gamboa, the oldest of six siblings to Mexican immigrants, decided she was definitely going to college.

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Newly developed mathematical techniques reveal important tools for data mining analysis.

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Drawing from data mining, image and video processing, statistics, and pattern recognition, these computational tools improve the way scientists extract useful information from data.

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This project's techniques reduce bandwidth requirements for large unstructured data by making use of data compression and optimizing the layout of the data for better locality and cache reuse.

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Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.

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New platforms are improving big data computing on Livermore’s high performance computers.

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The Earth System Grid Federation is a web-based tool set that powers most global climate change research.

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LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

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LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

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zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

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hzip 1.0.1 is a C++ library for lossless compression of structured and unstructured meshes composed of cells with hypercube topology.

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The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.

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This genome sequencing technology helps accelerate the comparison of genetic fragments with reference genomes and improve the accuracy of the results as compared to previous technologies.

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Jeene Villanueva develops enterprise modeling tools that help DOE decision makers gain insight into the challenging problems faced by the U.S. nuclear weapons complex.

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