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Perspectives on AI Architectures and Co-design for Earth System Predictability

Technical Report ·
DOI:https://doi.org/10.2172/2378014· OSTI ID:2378014
 [1];  [1];  [1];  [2];  [3];  [4];  [5];  [6];  [6];  [7];  [8]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  2. US Department of Energy (USDOE) National Nuclear Security Administration (NNSA), Washington, DC (United States)
  3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  4. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  5. Georgia Institute of Technology, Atlanta, GA (United States)
  6. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  7. KTH Royal Inst. of Technology, Stockholm (Sweden)
  8. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)
DOE Contract Number:
89233218CNA000001; AC05-76RL01830; AC52-07NA27344; AC05-00OR22725
OSTI ID:
2378014
Report Number(s):
LA-UR--23-22945; PNNL-SA--183269; LLNL--JRNL-846885
Country of Publication:
United States
Language:
English

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