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Title: Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

Abstract

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and transformative effects across the Department of Energy. Accordingly, the January 2018 Basic Research Needs workshop identified six Priority Research Directions (PRDs). The first three PRDs describe foundational research themes that correspond to the need for domain-awareness (PRD #1), interpretability (PRD #2), and robustness (PRD #3). The other three PRDs describe capability research themes and correspond to the three major use cases for massive scientific data analysis (PRD #4), machine learning-enhanced modeling and simulation (PRD #5), and intelligent automation and decision-support for complex systems (PRD #6). The Priority Research Directions provide a sound basis for a coherent, long-term research and development strategy in SciML and AI. Over the last decade, DOE investments in applied mathematics have laid the groundwork for the type of basic research that will underpin key advances in the six PRDs. Such advances will build on the work from leading researchers in optimization, linear algebra, high-performance solvers and algorithms, multiscale modeling and simulation, complex systems research, uncertainty quantification, and the new basic research areas that will emerge from the pursuit of transformative technologies.

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. Johns Hopkins Univ., Baltimore, MD (United States)
  6. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  7. Rutgers Univ., New Brunswick, NJ (United States)
  8. State University of New York (SUNY) Buffalo. Buffalo, NY (United States)
  9. University of California at Berkeley (UC Berkeley). Berkeley, CA (United States)
  10. Argonne National Laboratory (ANL). Lemont, IL (United States)
  11. University of Texas at Austin (UT Austin). Austin, Texas (United States)
Publication Date:
Research Org.:
USDOE Office of Science (SC), Washington, D.C. (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1478744
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Baker, Nathan, Alexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, and Willcox, Karen. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. United States: N. p., 2019. Web. doi:10.2172/1478744.
Baker, Nathan, Alexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, & Willcox, Karen. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. United States. doi:10.2172/1478744.
Baker, Nathan, Alexander, Frank, Bremer, Timo, Hagberg, Aric, Kevrekidis, Yannis, Najm, Habib, Parashar, Manish, Patra, Abani, Sethian, James, Wild, Stefan, and Willcox, Karen. Sun . "Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence". United States. doi:10.2172/1478744. https://www.osti.gov/servlets/purl/1478744.
@article{osti_1478744,
title = {Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence},
author = {Baker, Nathan and Alexander, Frank and Bremer, Timo and Hagberg, Aric and Kevrekidis, Yannis and Najm, Habib and Parashar, Manish and Patra, Abani and Sethian, James and Wild, Stefan and Willcox, Karen},
abstractNote = {Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and transformative effects across the Department of Energy. Accordingly, the January 2018 Basic Research Needs workshop identified six Priority Research Directions (PRDs). The first three PRDs describe foundational research themes that correspond to the need for domain-awareness (PRD #1), interpretability (PRD #2), and robustness (PRD #3). The other three PRDs describe capability research themes and correspond to the three major use cases for massive scientific data analysis (PRD #4), machine learning-enhanced modeling and simulation (PRD #5), and intelligent automation and decision-support for complex systems (PRD #6). The Priority Research Directions provide a sound basis for a coherent, long-term research and development strategy in SciML and AI. Over the last decade, DOE investments in applied mathematics have laid the groundwork for the type of basic research that will underpin key advances in the six PRDs. Such advances will build on the work from leading researchers in optimization, linear algebra, high-performance solvers and algorithms, multiscale modeling and simulation, complex systems research, uncertainty quantification, and the new basic research areas that will emerge from the pursuit of transformative technologies.},
doi = {10.2172/1478744},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {2}
}