Rick Archibald Applied Mathematician Contact 865.576.5761 | ARCHIBALDRK@ORNL.GOV All Publications Error-controlled, progressive, and adaptable retrieval of scientific data with multilevel decomposition AI-based design of a nuclear reactor core Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing Machine learning for neutron reflectometry data analysis of two-layer thin films Probing potential energy landscapes via electron-beam-induced single atom dynamics In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning and Experience Replay Machine Learning for Neutron Scattering at ORNL An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems AI Optimization of the Reactor Unit Cell to Support TCR Optimization... Thermal-Hydraulic Analyses to Support TCR Optimization A Stochastic Gradient Descent Approach for Stochastic Optimal Control Reconstruction of effective potential from statistical analysis of dynamic trajectories Artificial Intelligence Design of Nuclear Systems Empowered by Advanced Manufacturing Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques Super-resolution energy spectra from neutron direct-geometry spectrometers Artificial Intelligence Design of Nuclear Systems A direct filter method for parameter estimation BraggNet: integrating Bragg peaks using neural networks Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo Improving the accuracy and resolution of neutron crystallographic data by three-dimensional profile fitting of Bragg peaks in... Total variation-based neutron computed tomography An Effective Online Data Monitoring and Saving Strategy for Large-Scale Climate Simulations Performance Analysis of Fully Explicit and Fully Implicit Solvers Within a Spectral Element Shallow-water Atmosphere Model Pagination First page « First Previous page ‹â¶Ä¹ Page 1 Current page 2 Page 3 … 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
