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Neuro-Symbolic Bi-Directional Translation -- Deep Learning Explainability for Climate Tipping Point Research
In this work we propose a neuro-symbolic approach called Neuro-Symbolic Question-Answer Program Translator, or NS-QAPT, to address explainability and interpretability for deep learning climate simulation, applied to climate tipping point discovery.
A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)
We describe an adversarial game to explore the parameter space of these models, detect upcoming tipping points, and discover the drivers of tipping points.
Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points
We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).
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