Perspectives on AI Architectures and Codesign for Earth System Predictability
[1];
- a Pacific Northwest National Laboratory, Richland, Washington
- b National Nuclear Security Administration, Washington, D.C.
- c Lawrence Livermore National Laboratory, Livermore, California
- d Carnegie Mellon University, Pittsburgh, Pennsylvania
- e Georgia Institute of Technology, Atlanta, Georgia
- f Oak Ridge National Laboratory, Oak Ridge, Tennessee
- g KTH Royal Institute of Technology, Stockholm, Sweden
- h Los Alamos National Laboratory, Los Alamos, New Mexico
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:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC05-76RL01830; AC05-00OR22725; AC52-07NA27344
- OSTI ID:
- 2280461
- Alternate ID(s):
- OSTI ID: 2281198; OSTI ID: 2293477
- Report Number(s):
- PNNL-SA-183269; e230029
- Journal Information:
- Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Vol. 3 Journal Issue: 1; ISSN 2769-7525
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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