AC controlled spin-orbit qubits
Abstract not provided.
Publications
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Jump to search filtersCategorizing Trajectory Subsegments
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Computational Sciences and Math at Sandia -- Algorithms Software and Applications
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LAMMPS Tutorial
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Recent Developments in LAMMPS
Abstract not provided.
Kokkos Tutorial July 2018 Ibanez slides
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A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models
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Optimal Control of the Laser Melting Process using Risk Averse Optimization
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Slycat? VideoSwarm MLDL Workshop
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Open Source Software for HPC
The computational power of HPC is beyond our comprehension when we hear that 5 quadrillion computations can happen in a matter of seconds, or that machine learning is changing the way everything works. But none of that happens in a vacuum, and the teams behind the scenes—the developers of the hardware, the operating systems, the data transfer protocols, and the applications themselves—are the unsung heroes of a world where faster is better and you'd better hope there's no bug in the software or the hardware to slow you down. HPC is most successful when all these aspects work together seamlessly. The stories that follow are a tribute to the hardworking teams behind the scenes.
Advances in the Approximation Theory for Functional Reconstructions Using GMLS and Applications to Meshless Discretization of Differential Equations
Abstract not provided.
Evaluating the Intel Skylake Xeon Processor for HPC Workloads
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Single and Double Hole Quantum Dots in Ge/SiGe Quantum Wells
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Using Deep Learning to Classify Open Source Imagery
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Monte Carlo Simulation of Polycrystalline Additive Manufacturing Microstructures
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Spiking Neural Algorithms for Markov Process Random Walk
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Leveraging Containerization for DevOps with Sandia's HPC Workloads
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HPC Containerization with Singularity
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The Impact of Critical Defects on Material Performance and Science-Based Qualification for Metal Laser Powder Bed Fusion
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Static Source Code Analysis Using Neural Networks
Abstract not provided.
Spoke-darts for high-dimensional blue-noise sampling
ACM Transactions on Graphics
Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them. We present a blue noise sampling method with good quality and performance across different dimensions. The method, spoke-dart sampling, shoots rays from prior samples and selects samples from these rays. It combines the advantages of two major high-dimensional sampling methods: the locality of advancing front with the dimensionality-reduction of hyperplanes, specifically line sampling. We prove that the output sampling is saturated with high probability, with bounds on distances between pairs of samples and between any domain point and its nearest sample. We demonstrate spoke-dart applications for approximate Delaunay graph construction, global optimization, and robotic motion planning. Both the blue-noise quality of the output distribution and the adaptability of the intermediate processes of our method are useful in these applications.
HPC at Sandia: Exploring the Virtualization and Containerization of ARM64 Processors for Future HPC Workloads
Abstract not provided.
Modeling microstructure and defects with peridynamics
Abstract not provided.
Retinal Motion-Detection Under Noisy Conditions
Abstract not provided.
Mesoscale Modeling of Explosives at Sandia National Laboratories: Past and Future Directions
Abstract not provided.
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