Richard Archibald

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 19

Top Topics

Data Compression

Kernel Learning

Scientific Data

Top Venues

J. Comput. Phys.

Mach. Learn. Sci. Technol.

Publications

Siming Liang, Hui Sun, Richard Archibald, Feng Bao
Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm. CoRR (2024)
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
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. CoRR (2024)
Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun
Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks. SIAM J. Numer. Anal. 62 (2) (2024)
Richard Archibald, Feng Bao, Jiongmin Yong
A stochastic maximum principle approach for reinforcement learning with parameterized environment. J. Comput. Phys. 488 (2023)
Benjamin P. Russo, M. Paul Laiu, Richard Archibald
Streaming Compression of Scientific Data via weak-SINDy. CoRR (2023)
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
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX 24 (2023)
Mark R. Cianciosa, Richard Archibald, Wael R. Elwasif, Ana Gainaru, Jin Myung Park, Ross Whitfield
Adaptive Generation of Training Data for ML Reduced Model Creation. Big Data (2022)
Richard Archibald, Feng Bao
Kernel learning backward SDE filter for data assimilation. J. Comput. Phys. 455 (2022)
Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun
Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent. CoRR (2022)
Addi Malviya-Thakur, Seth Hitefield, Marshall 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
Towards a Software Development Framework for Interconnected Science Ecosystems. SMC (2022)
Zezhong Zhang, Richard Archibald, Feng Bao
A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems. CoRR (2022)
Richard Archibald, Feng Bao
A Kernel Learning Method for Backward SDE Filter. CoRR (2022)
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
Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression. SMC (2021)
Mathieu Doucet, Anjana M. Samarakoon, Changwoo Do, William T. Heller, Richard Archibald, D. Alan Tennant, Thomas Proffen, Garrett E. Granroth
Machine learning for neutron scattering at ORNL. Mach. Learn. Sci. Technol. 2 (2) (2021)
Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald
In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Mach. Learn. Sci. Technol. 2 (2) (2021)
Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang
Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle. CoRR (2020)
Richard Archibald, Feng Bao, Jiongmin Yong, Tao Zhou
An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems. J. Sci. Comput. 85 (2) (2020)
Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. IEEE BigData (2019)
Richard Archibald, Feng Bao, Xuemin Tu
A direct filter method for parameter estimation. J. Comput. Phys. 398 (2019)
Roisin Langan, Richard Archibald, Matthew Plumlee, Salil Mahajan, Daniel M. Ricciuto, Chengen Yang, Rui Mei, Jiafu Mao, Xiaoying Shi, Joshua S. Fu
Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling. ICCS (2014)
John D. Jakeman, Richard Archibald, Dongbin Xiu
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. J. Comput. Phys. 230 (10) (2011)