Building fast emulators in climate modeling
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Climate models are essential in policy making. Current climate models, however, are quite impractical for answering quick ‘what-if’ questions, because they are too slow. For example, ‘what-if we plant one trillion trees’ or ‘what if we increase the carbon tax’. This talk will overview a doctoral thesis defense on how deep learning could create approximations of climate models that can quickly answer such questions. We call those approximations ’emulators’. After introducing climate emulators, I will address a core challenge on how to develop emulators on physical, chaotic, multiscale data using Multiscale Neural Operators. Lastly, I will present a roadmap towards an online tool that uses deep learning emulators to create accessible simulations of climate policy impacts.
This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.