Reducing the effects of noise in MRI reconstruction. - Rick Archibald; Anne Gelb (ISBI, 2002)

Reducing the Gibbs ringing artifact in MRI scans while maintaining tissue boundary integrity. - Anne Gelb; Rick Archibald (ISBI, 2002)

Reducing the Effects of Noise in Image Reconstruction. - Rick Archibald; Anne Gelb (J. Sci. Comput., 2002)

A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity. - Rick Archibald; Anne Gelb (IEEE Trans. Medical Imaging, 2002)

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method. - Rick Archibald; Kewei Chen 001; Anne Gelb; Rosemary A. Renaut (NeuroImage, 2003)

Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction. - Rick Archibald; Jiuxiang Hu; Anne Gelb; Gerald E. Farin (IEEE Trans. Image Process., 2004)

Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images. - Rick Archibald; Anne Gelb; Jungho Yoon (SIAM J. Numer. Anal., 2005)

One-sided Post-processing for the Discontinuous Galerkin Method Using ENO Type Stencil Choosing and the Local Edge Detection Method. - Rick Archibald; Anne Gelb; Sigal Gottlieb; Jennifer K. Ryan (J. Sci. Comput., 2006)

Feature Selection and Classification of Hyperspectral Images With Support Vector Machines. - Rick Archibald; George Fann (IEEE Geosci. Remote. Sens. Lett., 2007)

Time Acceleration Methods for Advection on the Cubed Sphere. - Rick Archibald; Katherine J. Evans; J. B. Drake; James B. White III (ICCS (2), 2009)

Discontinuity detection in multivariate space for stochastic simulations. - Rick Archibald; Anne Gelb; Rishu Saxena; Dongbin Xiu (J. Comput. Phys., 2009)

Support Vector Machine-Based Endmember Extraction. - Anthony M. Filippi; Rick Archibald (IEEE Trans. Geosci. Remote. Sens., 2009)

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. - John D. Jakeman; Richard Archibald; Dongbin Xiu (J. Comput. Phys., 2011)

Characterizing the elements of Earth's radiative budget: Applying uncertainty quantification to the CESM. - Rick Archibald; M. Chakoumakos; Tony Zhuang (ICCS, 2012)

Progress towards accelerating HOMME on hybrid multi-core systems. - Ilene Carpenter; Rick Archibald; Katherine J. Evans; Jeffrey M. Larkin; Paulius Micikevicius; Matthew R. Norman; James Rosinski; Jim Schwarzmeier; Mark A. Taylor (Int. J. High Perform. Comput. Appl., 2013)

Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling. - Roisin Langan; Richard Archibald; Matthew Plumlee; Salil Mahajan; Daniel M. Ricciuto; Chengen Yang; Rui Mei; Jiafu Mao; Xiaoying Shi; Joshua S. Fu (ICCS, 2014)

Foreword. - Rick Archibald; Clayton G. Webster (Int. J. Comput. Math., 2014)

Emulation to simulate low-resolution atmospheric data. - Vishwas Rao; Rick Archibald; Katherine J. Evans (Int. J. Comput. Math., 2014)

Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs. - Rick Archibald; Katherine J. Evans; Andrew G. Salinger (ICCS, 2015)

Accelerated application development: The ORNL Titan experience. - Wayne Joubert; Rick Archibald; Mark A. Berrill; W. Michael Brown; Markus Eisenbach 002; Ray W. Grout; Jeff Larkin; John M. Levesque; Bronson Messer; Matthew R. Norman; Bobby Philip; Ramanan Sankaran; et al. (Comput. Electr. Eng., 2015)

Image Reconstruction from Fourier Data Using Sparsity of Edges. - Gabriel Wasserman; Rick Archibald; Anne Gelb (J. Sci. Comput., 2015)

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments. - Eric J. Lingerfelt; Alex Belianinov; Eirik Endeve; O. Ovchinnikov; Suhas Somnath; Jose M. Borreguero; N. Grodowitz; B. Park; Richard K. Archibald; Christopher T. Symons; Sergei V. Kalinin; O. E. Bronson Messer; et al. (ICCS, 2016)

Hierarchical optimization for neutron scattering problems. - Feng Bao 005; Rick Archibald; Dipanshu Bansal; Olivier Delaire (J. Comput. Phys., 2016)

Image Reconstruction from Undersampled Fourier Data Using the Polynomial Annihilation Transform. - Rick Archibald; Anne Gelb; Rodrigo B. Platte (J. Sci. Comput., 2016)

Model-based iterative reconstruction for neutron laminography. - S. V. Venkatakrishnan 001; Ercan Cakmak; Hassina Z. Bilheux; Philip R. Bingham; Richard K. Archibald (ACSSC, 2017)

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. - Sunwoo Lee 001; Qiao Kang; Sandeep Madireddy; Prasanna Balaprakash; Ankit Agrawal 001; Alok N. Choudhary; Richard Archibald; Wei-keng Liao (IEEE BigData, 2019)

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis. - Brendan Sullivan; Rick Archibald; Venu Vandavasi; Patricia Langan; Leighton Coates; Vickie Lynch (CCGRID, 2019)

Performance analysis of fully explicit and fully implicit solvers within a spectral element shallow-water atmosphere model. - Katherine J. Evans; Richard K. Archibald; David J. Gardner; Matthew R. Norman; Mark A. Taylor; Carol S. Woodward; Patrick H. Worley (Int. J. High Perform. Comput. Appl., 2019)

A direct filter method for parameter estimation. - Richard Archibald; Feng Bao 005; Xuemin Tu (J. Comput. Phys., 2019)

Integrating Deep Learning in Domain Sciences at Exascale. - Rick Archibald; Edmond Chow; Eduardo F. D'Azevedo; Jack J. Dongarra; Markus Eisenbach 002; Rocco Febbo; Florent Lopez; Daniel Nichols; Stanimire Tomov; Kwai Wong; Junqi Yin (SMC, 2020)

Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle. - Richard Archibald; Feng Bao 005; Yanzhao Cao; He Zhang (CoRR, 2020)

An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems. - Richard Archibald; Feng Bao 005; Jiongmin Yong; Tao Zhou (J. Sci. Comput., 2020)

Error-controlled, progressive, and adaptable retrieval of scientific data with multilevel decomposition. - Xin Liang 001; Qian Gong; Jieyang Chen; Ben Whitney; Lipeng Wan 001; Qing Liu 002; David Pugmire; Rick Archibald; Norbert Podhorszki; Scott Klasky (SC, 2021)

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression. - Qian Gong; Xin Liang 001; Ben Whitney; Jong Youl Choi; Jieyang Chen; Lipeng Wan 001; Stéphane Ethier; Seung-Hoe Ku; Randy Michael Churchill; Choong-Seock Chang; Mark Ainsworth; Ozan Tugluk; et al. (SMC, 2021)

Machine learning for neutron reflectometry data analysis of two-layer thin films. - Mathieu Doucet; Richard K. Archibald; William T. Heller (Mach. Learn. Sci. Technol., 2021)

Machine learning for neutron scattering at ORNL. - Mathieu Doucet; Anjana M. Samarakoon; Changwoo Do; William T. Heller; Richard Archibald; D. Alan Tennant; Thomas Proffen; Garrett E. Granroth (Mach. Learn. Sci. Technol., 2021)

In situ compression artifact removal in scientific data using deep transfer learning and experience replay. - Sandeep Madireddy; Ji Hwan Park; Sunwoo Lee 001; Prasanna Balaprakash; Shinjae Yoo; Wei-keng Liao; Cory D. Hauck; M. Paul Laiu; Richard Archibald (Mach. Learn. Sci. Technol., 2021)

Sas-temper: Software for fitting small-angle scattering data that provides automated reproducibility characterization. - William T. Heller; Mathieu Doucet; Richard K. Archibald (SoftwareX, 2021)

Improving Predictions Under Uncertainty of Material Plasma Device Operations. - Rick Archibald; Mark R. Cianciosa; Cornwall Lau (IEEE Big Data, 2022)

Adaptive Generation of Training Data for ML Reduced Model Creation. - Mark R. Cianciosa; Richard Archibald; Wael R. Elwasif; Ana Gainaru; Jin Myung Park; Ross Whitfield (IEEE Big Data, 2022)

Towards a Software Development Framework for Interconnected Science Ecosystems. - Addi Malviya-Thakur; Seth Hitefield; Marshall T. McDonnell; Matthew Wolf; Richard Archibald; Lance Drane; Kevin Roccapriore; Maxim A. Ziatdinov; Jesse McGaha; Robert Smith; John Hetrick; Mark Abraham; et al. (SMC, 2022)

A Kernel Learning Method for Backward SDE Filter. - Richard Archibald; Feng Bao 005 (CoRR, 2022)

A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems. - Zezhong Zhang; Richard Archibald; Feng Bao 005 (CoRR, 2022)

Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent. - Richard Archibald; Feng Bao 005; Yanzhao Cao; Hui Sun (CoRR, 2022)

Kernel learning backward SDE filter for data assimilation. - Richard Archibald; Feng Bao 005 (J. Comput. Phys., 2022)

A stochastic maximum principle approach for reinforcement learning with parameterized environment. - Richard Archibald; Feng Bao 005; Jiongmin Yong (J. Comput. Phys., 2023)

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. - Qian Gong; Jieyang Chen; Ben Whitney; Xin Liang 001; Viktor Reshniak; Tania Banerjee; Jaemoon Lee; Anand Rangarajan 001; Lipeng Wan 001; Nicolas Vidal 003; Qing Liu 002; Ana Gainaru; et al. (SoftwareX, 2023)

Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control. - Nicolas Vidal 003; Qian Gong; Viktor Reshniak; Rick Archibald; Scott Klasky (IEEE Big Data, 2024)

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems. - Rick Archibald; Addi Malviya-Thakur; Marshall T. McDonnell; Gregory Cage; Cody Stiner; Lance Drane; M. Paul Laiu; Michael J. Brim; Mathieu Doucet; William T. Heller; Ryan Coffee (IEEE Big Data, 2024)

A Framework for Compressing Unstructured Scientific Data via Serialization. - Viktor Reshniak; Qian Gong; Rick Archibald; Scott Klasky; Norbert Podhorszki (IEEE Big Data, 2024)

A General Framework for Error-controlled Unstructured Scientific Data Compression. - Qian Gong; Zhe Wang 059; Viktor Reshniak; Xin Liang 001; Jieyang Chen; Qing Liu 002; Tushar M. Athawale; Yi Ju; Anand Rangarajan 001; Sanjay Ranka; Norbert Podhorszki; Rick Archibald; et al. (e-Science, 2024)

Sparse L1-Autoencoders for Scientific Data Compression. - Matthias Chung; Rick Archibald; Paul J. Atzberger; Jack Michael Solomon (CoRR, 2024)

Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm. - Siming Liang; Hui Sun; Richard Archibald; Feng Bao 005 (CoRR, 2024)

Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order. - Viktor Reshniak; Evan Ferguson; Qian Gong; Nicolas Vidal 003; Rick Archibald; Scott Klasky (CoRR, 2024)

Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks. - Richard Archibald; Feng Bao 005; Yanzhao Cao; Hui Sun (SIAM J. Numer. Anal., 2024)

Haar-Like Wavelets on Hierarchical Trees. - Rick Archibald; Ben Whitney (J. Sci. Comput., 2024)

Integrating scientific single-page applications with DevSecOps. - Lance Drane; Marshall T. McDonnell; Randall Petras; Cody Stiner; Arthur J. Ruckman; Gavin M. Wiggins; Gregory Cage; Robert Smith; Seth Hitefield; Jesse McGaha; Andrew Ayres; Michael J. Brim; et al. (Future Gener. Comput. Syst., 2025)

Streaming Compression of Scientific Data via Weak-SINDy. - Benjamin P. Russo; M. Paul Laiu; Richard Archibald (SIAM J. Sci. Comput., 2025)

Federated Learning on Stochastic Neural Networks. - Jingqiao Tang; Ryan Bausback; Feng Bao 005; Richard Archibald (CoRR, 2025)

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research. - Ahmed Almeldein; Mohammed Alnaggar; Rick Archibald; Tom Beck; Arpan Biswas; Rike Bostelmann; Wes Brewer; Chris Bryan; Christopher Calle; Cihangir Celik; Rajni Chahal; Jong Youl Choi; et al. (CoRR, 2025)