LLNL researchers have developed additive manufactured fuel targets for IFE.  They have been successful in using TPL to fabricate low density (down to 60 mg/cm3) and low atomic number (CHO) polymeric foams for potential targets, and some have been tested at the OMEGA Laser Facility. With TPL, LLNL researchers have also been able to fabricate a full fuel capsule with diameter of ~ 5mm or…

This invention configures multiple spherical substrate targets to roll independently of one another.  The spheres’ rolling motion is deliberately randomized to promote uniform coating while eliminating the interaction (rubbing, sliding) of adjacent spheres that is present in conventional sphere coating designs. The devices’ novel structure features enable the collimation of depositing…

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…