Randomized Algorithms for Scientific Computing (RASC)
Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony, DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E., Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson, Juan M. Restrepo, C., Seshadhri, Draguna Vrabie, Brendt Wohlberg
TL;DR
This paper reviews the role of randomized algorithms in scientific computing, emphasizing their importance in advancing AI for Science across various DOE priority areas like climate, astrophysics, and quantum computing.
Contribution
It summarizes the outcomes of the RASC workshop, highlighting recent developments and future directions for randomized algorithms in scientific computing.
Findings
Randomized algorithms are crucial for tackling complexity in scientific problems.
Workshop outcomes identify key challenges and research opportunities.
Advances will impact climate science, astrophysics, and quantum computing.
Abstract
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021.
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