Rick Archibald Applied Mathematician Contact 865.576.5761 | ARCHIBALDRK@ORNL.GOV All Publications Model-based Iterative Reconstruction for Neutron Laminography Exascale Data Analytics for the DOE Recovery guarantees for compressed sensing with unknown errors Complex Optimization for Big Computational and Experimental Neutron Datasets... Big, deep, and smart data in scanning probe microscopy Distortion Correction in Scanning Transmission Electron Microcopy with Controllable Scanning Pathways Dynamic scan control in STEM: Spiral scans BEAM: A computational workflow system for managing and modeling material characterization data in HPC environments Hierarchical optimization for neutron scattering problems BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments Nuclear Forensics Analysis with Missing and Uncertain Data... Evaluating the Relationship between the Population Trends, Prices, Heat Waves, and the Demands of Energy Consumption in Cities Big-deep-smart data in imaging for guiding materials design... Accelerated Application Development: The ORNL Titan Experience Image Reconstruction from Under sampled Fourier Data Using the Polynomial Annihilation Transform Accelerating Time Integration for Climate Modeling Using GPUs An Adaptive Fourier Filter for Relaxing Time Stepping Constraints for Explicit Solvers Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets Analysis and Feature Detection in Large Volumes of Diffuse X-ray and Neutron Scattering from Complex Materials Accelerated Application Development: The ORNL Titan Experience Image Reconstruction from Fourier Data Using Sparsity of Edges Polynomial Annihilation Sparsifying Transform Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling... Emulation to simulate low resolution atmospheric data... Error estimation in high dimensional space for stochastic collocation methods on arbitrary sparse samples Error estimation in high dimensional space for stochastic collocation methods on arbitrary sparse samples Pagination First page « First Previous page ‹â¶Ä¹ … Page 2 Current page 3 Page 4 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Mathematics in Computation Section Data Analysis and Machine Learning Group
