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Physics-Informed Deep Learning for Multiscale Water Cycle Prediction

Technical Report ·
DOI:https://doi.org/10.2172/1769760· OSTI ID:1769760
 [1];  [2];  [3];  [4]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Clemson Univ., SC (United States)
  3. Argonne National Lab. (ANL), Argonne, IL (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
+ Show Author Affiliations

This paper falls under Focal Area #2: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection).

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Clemson Univ., SC (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI ID:
1769760
Report Number(s):
AI4ESP1100
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING