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Found 6 papers, 3 papers with code
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics
The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network.
Objective discovery of dominant dynamical processes with intelligible machine learning
The advent of big data has vast potential for discovery in natural phenomena ranging from climate science to medicine, but overwhelming complexity stymies insight.
Will Artificial Intelligence supersede Earth System and Climate Models?
We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids.
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning
In this region, THOR specifically reveals a shift in dynamical regime under climate change driven by changes in wind stress and interactions with bathymetry.
The Importance of Architecture Choice in Deep Learning for Climate Applications
Machine Learning has become a pervasive tool in climate science applications.
