LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects.  DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications. 

The approach is to use peroxides to modify the reaction kinetics in the production of polysiloxanes.  A radical initiator in the presence of a hydride-terminated polysiloxane will increase the rate of curing and reduce manufacturing costs.  At a minimum a formulation would contain a hydride-terminated polysiloxane, a platinum catalyst, and an initiator that generates radicals.  The content of…

The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…

LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…

LLNL researchers have developed a custom resin formulation which uses a dispersing solvent and only a multifunctional monomer as the binding agent. The dispersing solvent system typically used has multiple components meant to achieve excellent dispersal of silica in order to create a flowable resin (rather than a paste). The dispersing agent has low vapor pressure, which allows the 3D printed…