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Title: A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

Abstract

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

Authors:
 [1];  [2];  [1];  [3];  [2];  [4]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Univ. of Washington, Seattle, WA (United States)
  3. Univ. of California, Davis, CA (United States)
  4. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
Artificial Intelligence for Earth System Predictability (AI4ESP) Collaboration (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1769651
Report Number(s):
AI4ESP-1138
DOE Contract Number:  
35604.1
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES

Citation Formats

Voisin, Nathalie, Bennett, Andrew, Fang, Yilin, Nearing, Grey, Nijssen, Bart, and Rao, Yuhan. A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories. United States: N. p., 2021. Web. doi:10.2172/1769651.
Voisin, Nathalie, Bennett, Andrew, Fang, Yilin, Nearing, Grey, Nijssen, Bart, & Rao, Yuhan. A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories. United States. https://doi.org/10.2172/1769651
Voisin, Nathalie, Bennett, Andrew, Fang, Yilin, Nearing, Grey, Nijssen, Bart, and Rao, Yuhan. 2021. "A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories". United States. https://doi.org/10.2172/1769651. https://www.osti.gov/servlets/purl/1769651.
@article{osti_1769651,
title = {A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories},
author = {Voisin, Nathalie and Bennett, Andrew and Fang, Yilin and Nearing, Grey and Nijssen, Bart and Rao, Yuhan},
abstractNote = {The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.},
doi = {10.2172/1769651},
url = {https://www.osti.gov/biblio/1769651}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {4}
}