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.
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LLNL’s novel approach utilizes a number of techniques to improve reconstruction accuracy:
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 pioneered the use of tomographic reconstruction to determine the power density of electron beams using profiles of the beam taken at a number of angles. LLNL’s earlier diagnostic consisted of a fixed number of radially oriented sensor slits and required the beam to be circled over them at a fixed known diameter to collect data. The new sensor design incorporates annular slits instead,…