NEURAL OPERATORS FOR FAST WEATHER AND CLIMATE PREDICTIONS

Initial and boundary conditions, and parameters associated with geophysical modeling can be received. Based on the received initial and boundary conditions and parameters, a multiscale model can be trained for data generation to produce first resolution simulation data and second resolution simulati...

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Bibliographic Details
Main Authors Junior, Alberto Costa Nogueira, Holgate, Simon, Weldemariam, Komminist, Lütjens, Björn, Watson, Campbell D, Crawford, Catherine H
Format Patent
LanguageEnglish
Published 22.06.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Initial and boundary conditions, and parameters associated with geophysical modeling can be received. Based on the received initial and boundary conditions and parameters, a multiscale model can be trained for data generation to produce first resolution simulation data and second resolution simulation data for a surrogate machine learning model training, where the second resolution simulation data has higher resolution than the first resolution simulation data. A surrogate model can be created using neural operators, where the surrogate model is trained using the first resolution simulation data and second resolution simulation data. An operational forecasting model can be generated using the surrogate model.
Bibliography:Application Number: US202117557666