تقرير
Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points
| العنوان: | Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points |
|---|---|
| المؤلفون: | Sleeman, Jennifer, Chung, David, Ashcraft, Chace, Brett, Jay, Gnanadesikan, Anand, Kevrekidis, Yannis, Hughes, Marisa, Haine, Thomas, Pradal, Marie-Aude, Gelderloos, Renske, Tang, Caroline, Saksena, Anshu, White, Larry |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science |
| الوصف: | We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. 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). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible. Comment: This is the preprint of work presented at the 2022 AAAI Fall Symposium Series, Third Symposium on Knowledge-Guided ML, November 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.06852 |
| رقم الأكسشن: | edsarx.2302.06852 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |