Rapid monolith development at scale is achieved through use of a functionally equivalent optic simulant made from a low-cost material to substitute the functional optic. Monolith optical performance is affected not only by thermal expansion but also by temperature inhomogeneity due to the temperature dependence of refractive index.

Aeroptics are a proposed new class of monolithic optical system in aerogel fabricated by molding around a master mandrel. This approach combines the intrinsic stability of proven monolithic telescopes, with the ultralow density of silica aerogels. In Aeroptics, the monolith is hollow with an aerogel substrate providing a supporting structure. Theoretically, Aeroptics could enable 1-m aperture…

This invention achieves both a wider field of view and faster f-number within a monolithic substrate by incorporating an aspheric convex refractive first surface and a planar aspheric field corrector surface on the final refractive surface. These two refractive surfaces work in conjunction with a concave aspheric primary and convex aspheric secondary mirror (e.g. Cassegrain type) to improve…

Many of the disadvantages of current interface devices can be overcome with LLNL’s novel interface design, which relies on area array distribution where independent interface connector subassemblies are positioned in a planar grid.  Not only is the interface device expandable area-wise (without increasing contact force), but it could also be expanded height-wise, with multiple layers of these…

Commercial fiber optic cables are the current standard for carrying optical signals in industries like communications or medical devices. However, the fibers are made of glass, which do not have favorable characteristics for applications that require flexibility and re-routing, e.g. typically brittle, limited selection of materials, dimension constraints.

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…

Monolithic Telescopes are a novel implementation of a solid catadioptric design form, instantiated in a monolithic block of fused silica.

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…

LLNL has developed a brain-on-a-chip system with a removable cell-seeding funnel to simultaneously localize neurons from various brain regions in an anatomically relevant manner and over specific electrode regions of a MEA. LLNL’s novel, removable cell seeding funnel uses a combination of 3D printing and microfabrication that allows neurons from select brain regions to easily be seeded into…

LLNL is developing the Space-based Telescopes for Actionable Refinement of Ephemeris (STARE). STARE is a constellation of low cost nano-satellites (less than 5Kg) in low-earth orbit dedicated to the observation of space debris in conjunction with a ground-based infrastructure for maintenance, coordination and data processing. Each nano-satellite in the constellation is capable of recording an…