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Keyword: physics-guided machine learning ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods

   Lopez‐Gomez, Christopoulos, Ervik, Dunbar, Cohen and Schneider · Journal of Advances in Modeling Earth Systems · 2022

   The paper shows that two Kalman inversion methods can train physics-based and machine-learning parameterizations without gradients, and tests them by calibrating a turbulence and convection scheme against large-eddy simulations.

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   1. UncheckedUnscented Kalman inversion, one of two gradient-free training methods tested, also gives a measure of how uncertain the fitted parameters are.“In addition, unscented Kalman inversion provides a measure of parameter uncertainty.”
   2. Unchecked“We find the algorithms amenable to batching strategies, robust to noise and model failures, and efficient in the calibration of hybrid parameterizations that can include empirical closures and neural networks.”