LLNL’s Computing & Communications portfolio is a gateway for accessing LLNL’s wide variety of solutions and intellectual property for use in information technologies, communications, quantum sciences, data sciences and applied software/modeling & simulations. LLNL’s long history and strong capabilities in computing underpin our success in research, in developing new solutions for our missions, and in our collaborations with the academic and private sectors. We license solutions via diverse mechanisms suited to the use cases, ranging from open-source software licensing, to nonexclusive end user licenses, to custom proprietary licenses for distributors, startups, and other commercialization licensees. We also collaborate with industry partners interested in applying LLNL’s unique capabilities and computing solutions to their company’s challenges.
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Lawrence Livermore National Laboratory has developed GridDS — an open-source, data-science toolkit for power and data engineers that will provide an integrated energy data storage and augmentation infrastructure, as well as a flexible and comprehensive set of state-of-the-art machine-learning models.
With business applications moving to the cloud from traditional corporate networks, a crucial part of any organization’s cybersecurity is managing the users who can access their computers, networks, software applications and data. LLNL’s One ID technology is a cost-effective way to more easily manage a large organization’s enterprise security.
Join us to hear about the latest in CT image reconstruction and data processing. Medical imaging, industrial manufacturing inspection and airport luggage security rely on CT. LLNL researchers have developed an innovative software product that betters the competition in imaging fidelity.
Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.
Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…
Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…
MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…
LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…