Rick Archibald

According to our database, Rick Archibald authored at least 54 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Haar-Like Wavelets on Hierarchical Trees.

Rick Archibald, Ben Whitney

J. Sci. Comput., April, 2024

Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks.

Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun

SIAM J. Numer. Anal., 2024

Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order.

Viktor Reshniak, Evan Ferguson, Qian Gong, Nicolas Vidal, Rick Archibald, Scott Klasky

CoRR, 2024

Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm.

Siming Liang, Hui Sun, Richard Archibald, Feng Bao

CoRR, 2024

Sparse L¹-Autoencoders for Scientific Data Compression.

Matthias Chung, Rick Archibald, Paul Atzberger, Jack Michael Solomon

CoRR, 2024

A General Framework for Error-controlled Unstructured Scientific Data Compression.

Qian Gong, Zhe Wang, Viktor Reshniak, Xin Liang, Jieyang Chen, Qing Liu, Tushar M. Athawale, Yi Ju, Anand Rangarajan, Sanjay Ranka, Norbert Podhorszki, Rick Archibald, Scott Klasky

Proceedings of the 20th IEEE International Conference on e-Science, 2024

2023

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring.

Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky

SoftwareX, December, 2023

A stochastic maximum principle approach for reinforcement learning with parameterized environment.

Richard Archibald, Feng Bao, Jiongmin Yong

J. Comput. Phys., September, 2023

Streaming Compression of Scientific Data via weak-SINDy.

Benjamin P. Russo, M. Paul Laiu, Richard Archibald

CoRR, 2023

2022

Kernel learning backward SDE filter for data assimilation.

Richard Archibald, Feng Bao

J. Comput. Phys., 2022

Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent.

Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun

CoRR, 2022

A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems.

Zezhong Zhang, Richard Archibald, Feng Bao

CoRR, 2022

A Kernel Learning Method for Backward SDE Filter.

Richard Archibald, Feng Bao

CoRR, 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, Sergey Yakubov, Gregory R. Watson, Ben Chance, Clara Nguyen, Matthew Baker, J. Robert Michael, Elke Arenholz, Ben Mintz

Proceedings of the Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, 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

Proceedings of the IEEE International Conference on Big Data, 2022

Improving Predictions Under Uncertainty of Material Plasma Device Operations.

Rick Archibald, Mark R. Cianciosa, Cornwall Lau

Proceedings of the IEEE International Conference on Big Data, 2022

2021

<i>Sas-temper</i>: Software for fitting small-angle scattering data that provides automated reproducibility characterization.

William T. Heller, Mathieu Doucet, Richard K. Archibald

SoftwareX, 2021

In situ compression artifact removal in scientific data using deep transfer learning and experience replay.

Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald

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

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

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression.

Qian Gong, Xin Liang, Ben Whitney, Jong Youl Choi, Jieyang Chen, Lipeng Wan, Stéphane Ethier, Seung-Hoe Ku, Randy Michael Churchill, Choong-Seock Chang, Mark Ainsworth, Ozan Tugluk, Todd S. Munson, David Pugmire, Richard Archibald, Scott Klasky

Proceedings of the Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, 2021

Error-controlled, progressive, and adaptable retrieval of scientific data with multilevel decomposition.

Xin Liang, Qian Gong, Jieyang Chen, Ben Whitney, Lipeng Wan, Qing Liu, David Pugmire, Rick Archibald, Norbert Podhorszki, Scott Klasky

Proceedings of the International Conference for High Performance Computing, 2021

2020

An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems.

Richard Archibald, Feng Bao, Jiongmin Yong, Tao Zhou

J. Sci. Comput., 2020

Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle.

Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang

CoRR, 2020

Integrating Deep Learning in Domain Sciences at Exascale.

Rick Archibald, Edmond Chow, Eduardo F. D'Azevedo, Jack J. Dongarra, Markus Eisenbach, Rocco Febbo, Florent Lopez, Daniel Nichols, Stanimire Tomov, Kwai Wong, Junqi Yin

Proceedings of the Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, 2020

2019

A direct filter method for parameter estimation.

Richard Archibald, Feng Bao, Xuemin Tu

J. Comput. Phys., 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

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis.

Brendan Sullivan, Rick Archibald, Venu Vandavasi, Patricia Langan, Leighton Coates, Vickie Lynch

Proceedings of the 19th IEEE/ACM International Symposium on Cluster, 2019

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time.

Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2017

Model-based iterative reconstruction for neutron laminography.

S. V. Venkatakrishnan, Ercan Cakmak, Hassina Z. Bilheux, Philip R. Bingham, Richard K. Archibald

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Image Reconstruction from Undersampled Fourier Data Using the Polynomial Annihilation Transform.

Rick Archibald, Anne Gelb, Rodrigo B. Platte

J. Sci. Comput., 2016

Hierarchical optimization for neutron scattering problems.

Feng Bao, Rick Archibald, Dipanshu Bansal, Olivier Delaire

J. Comput. Phys., 2016

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, Mallikarjun Shankar, Stephen Jesse

Proceedings of the International Conference on Computational Science 2016, 2016

2015

Image Reconstruction from Fourier Data Using Sparsity of Edges.

Gabriel Wasserman, Rick Archibald, Anne Gelb

J. Sci. Comput., 2015

Accelerated application development: The ORNL Titan experience.

Wayne Joubert, Rick Archibald, Mark A. Berrill, W. Michael Brown, Markus Eisenbach, Ray W. Grout, Jeff Larkin, John M. Levesque, Bronson Messer, Matthew R. Norman, Bobby Philip, Ramanan Sankaran, Arnold N. Tharrington, John A. Turner

Comput. Electr. Eng., 2015

Accelerating Time Integration for the Shallow Water Equations on the Sphere Using GPUs.

Rick Archibald, Katherine J. Evans, Andrew G. Salinger

Proceedings of the International Conference on Computational Science, 2015

2014

Emulation to simulate low-resolution atmospheric data.

Vishwas Rao, Rick Archibald, Katherine J. Evans

Int. J. Comput. Math., 2014

Foreword.

Rick Archibald, Clayton G. Webster

Int. J. Comput. Math., 2014

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

Proceedings of the International Conference on Computational Science, 2014

2013

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

2012

Characterizing the elements of Earth's radiative budget: Applying uncertainty quantification to the CESM.

Rick Archibald, M. Chakoumakos, Tony Zhuang

Proceedings of the International Conference on Computational Science, 2012

2011

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids.

John D. Jakeman, Richard Archibald, Dongbin Xiu

J. Comput. Phys., 2011

2009

Support Vector Machine-Based Endmember Extraction.

Anthony M. Filippi, Rick Archibald

IEEE Trans. Geosci. Remote. Sens., 2009

Discontinuity detection in multivariate space for stochastic simulations.

Rick Archibald, Anne Gelb, Rishu Saxena, Dongbin Xiu

J. Comput. Phys., 2009

Time Acceleration Methods for Advection on the Cubed Sphere.

Rick Archibald, Katherine J. Evans, J. B. Drake, James B. White III

Proceedings of the Computational Science, 2009

2007

Feature Selection and Classification of Hyperspectral Images With Support Vector Machines.

Rick Archibald, George Fann

IEEE Geosci. Remote. Sens. Lett., 2007

2006

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

2005

Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images.

Rick Archibald, Anne Gelb, Jungho Yoon

SIAM J. Numer. Anal., 2005

2004

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

2003

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method.

Rick Archibald, Kewei Chen, Anne Gelb, Rosemary A. Renaut

NeuroImage, 2003

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

Reducing the Effects of Noise in Image Reconstruction.

Rick Archibald, Anne Gelb

J. Sci. Comput., 2002

Reducing the Gibbs ringing artifact in MRI scans while maintaining tissue boundary integrity.

Anne Gelb, Rick Archibald

Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging, 2002

Reducing the effects of noise in MRI reconstruction.

Rick Archibald, Anne Gelb

Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging, 2002