Richard Archibald

24 papers B 3Misc 1Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Federated Learning on Stochastic Neural Networks. CoRR Jingqiao Tang, Ryan Bausback, Feng Bao, Richard Archibald
2025	J	`jnl`	Integrating scientific single-page applications with DevSecOps. Future Gener. Comput. Syst. 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, Richard Archibald, Addi Malviya-Thakur
2025	J	`jnl`	Streaming Compression of Scientific Data via Weak-SINDy. SIAM J. Sci. Comput. Benjamin P. Russo, M. Paul Laiu, Richard Archibald
2024	J	`jnl`	Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm. CoRR Siming Liang, Hui Sun, Richard Archibald, Feng Bao
2024	J	`jnl`	MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. CoRR 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
2024	J	`jnl`	Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks. SIAM J. Numer. Anal. Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun
2023	J	`jnl`	A stochastic maximum principle approach for reinforcement learning with parameterized environment. J. Comput. Phys. Richard Archibald, Feng Bao, Jiongmin Yong
2023	J	`jnl`	MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX 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
2023	J	`jnl`	Streaming Compression of Scientific Data via weak-SINDy. CoRR Benjamin P. Russo, M. Paul Laiu, Richard Archibald
2022	J	`jnl`	A Kernel Learning Method for Backward SDE Filter. CoRR Richard Archibald, Feng Bao
2022	J	`jnl`	A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems. CoRR Zezhong Zhang, Richard Archibald, Feng Bao
2022	B	`conf`	Adaptive Generation of Training Data for ML Reduced Model Creation. IEEE Big Data Mark R. Cianciosa, Richard Archibald, Wael R. Elwasif, Ana Gainaru, Jin Myung Park, Ross Whitfield
2022	J	`jnl`	Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent. CoRR Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun
2022	J	`jnl`	Kernel learning backward SDE filter for data assimilation. J. Comput. Phys. Richard Archibald, Feng Bao
2022	B	`conf`	Towards a Software Development Framework for Interconnected Science Ecosystems. SMC 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
2021	J	`jnl`	In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Mach. Learn. Sci. Technol. Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald
2021	J	`jnl`	Machine learning for neutron scattering at ORNL. Mach. Learn. Sci. Technol. Mathieu Doucet, Anjana M. Samarakoon, Changwoo Do, William T. Heller, Richard Archibald, D. Alan Tennant, Thomas Proffen, Garrett E. Granroth
2021	B	`conf`	Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression. SMC 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
2020	J	`jnl`	An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems. J. Sci. Comput. Richard Archibald, Feng Bao, Jiongmin Yong, Tao Zhou
2020	J	`jnl`	Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle. CoRR Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang
2019	J	`jnl`	A direct filter method for parameter estimation. J. Comput. Phys. Richard Archibald, Feng Bao, Xuemin Tu
2019	—	`conf`	Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. IEEE BigData Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao
2014	Misc	`conf`	Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling. ICCS Roisin Langan, Richard Archibald, Matthew Plumlee, Salil Mahajan, Daniel M. Ricciuto, Chengen Yang, Rui Mei, Jiafu Mao, Xiaoying Shi, Joshua S. Fu
2011	J	`jnl`	Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids. J. Comput. Phys. John D. Jakeman, Richard Archibald, Dongbin Xiu